Learning to Understand Phrases by Embedding the Dictionary

Felix Hill
Computer Laboratory

University of Cambridge
felix.hill@cl.cam.ac.uk

Kyunghyun Cho∗
Courant Institute of Mathematical Sciences

and Centre for Data Science
New York University

kyunghyun.cho@nyu.edu

Anna Korhonen
Department of Theoretical and Applied Linguistics

University of Cambridge
alk23@cam.ac.uk

Yoshua Bengio
CIFAR Senior Fellow

Université de Montréal
yoshua.bengio@umontreal.ca

Abstract

Distributional models that learn rich seman-
tic word representations are a success story
of recent NLP research. However, develop-
ing models that learn useful representations of
phrases and sentences has proved far harder.
We propose using the definitions found in
everyday dictionaries as a means of bridg-
ing this gap between lexical and phrasal se-
mantics. Neural language embedding mod-
els can be effectively trained to map dictio-
nary definitions (phrases) to (lexical) repre-
sentations of the words defined by those defi-
nitions. We present two applications of these
architectures: reverse dictionaries that return
the name of a concept given a definition or
description and general-knowledge crossword
question answerers. On both tasks, neural lan-
guage embedding models trained on defini-
tions from a handful of freely-available lex-
ical resources perform as well or better than
existing commercial systems that rely on sig-
nificant task-specific engineering. The re-
sults highlight the effectiveness of both neu-
ral embedding architectures and definition-
based training for developing models that un-
derstand phrases and sentences.

1 Introduction

Much recent research in computational seman-
tics has focussed on learning representations of
arbitrary-length phrases and sentences. This task is
challenging partly because there is no obvious gold
standard of phrasal representation that could be used

∗ Work mainly done at the University of Montreal.

in training and evaluation. Consequently, it is diffi-
cult to design approaches that could learn from such
a gold standard, and also hard to evaluate or compare
different models.

In this work, we use dictionary definitions to ad-
dress this issue. The composed meaning of the
words in a dictionary definition (a tall, long-necked,
spotted ruminant of Africa) should correspond to
the meaning of the word they define (giraffe). This
bridge between lexical and phrasal semantics is use-
ful because high quality vector representations of
single words can be used as a target when learning
to combine the words into a coherent phrasal repre-
sentation.

This approach still requires a model capable of
learning to map between arbitrary-length phrases
and fixed-length continuous-valued word vectors.
For this purpose we experiment with two broad
classes of neural language models (NLMs): Recur-
rent Neural Networks (RNNs), which naturally en-
code the order of input words, and simpler (feed-
forward) bag-of-words (BOW) embedding models.
Prior to training these NLMs, we learn target lexi-
cal representations by training the Word2Vec soft-
ware (Mikolov et al., 2013) on billions of words of
raw text.

We demonstrate the usefulness of our approach by
building and releasing two applications. The first is
a reverse dictionary or concept finder: a system that
returns words based on user descriptions or defini-
tions (Zock and Bilac, 2004). Reverse dictionaries
are used by copywriters, novelists, translators and
other professional writers to find words for notions
or ideas that might be on the tip of their tongue.

17

Transactions of the Association for Computational Linguistics, vol. 4, pp. 17–30, 2016. Action Editor: Chris Callison-Burch.
Submission batch: 9/2015; revised 12/2015; revised 1/2016; Published 2/2016.

c©2016 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license.



For instance, a travel-writer might look to enhance
her prose by searching for examples of a country
that people associate with warm weather or an ac-
tivity that is mentally or physically demanding. We
show that an NLM-based reverse dictionary trained
on only a handful of dictionaries identifies novel def-
initions and concept descriptions comparably or bet-
ter than commercial systems, which rely on signif-
icant task-specific engineering and access to much
more dictionary data. Moreover, by exploiting mod-
els that learn bilingual word representations (Vulic
et al., 2011; Klementiev et al., 2012; Hermann and
Blunsom, 2013; Gouws et al., 2014), we show that
the NLM approach can be easily extended to pro-
duce a potentially useful cross-lingual reverse dic-
tionary.

The second application of our models is as a
general-knowledge crossword question answerer.
When trained on both dictionary definitions and the
opening sentences of Wikipedia articles, NLMs pro-
duce plausible answers to (non-cryptic) crossword
clues, even those that apparently require detailed
world knowledge. Both BOW and RNN models can
outperform bespoke commercial crossword solvers,
particularly when clues contain a greater number of
words. Qualitative analysis reveals that NLMs can
learn to relate concepts that are not directly con-
nected in the training data and can thus generalise
well to unseen input. To facilitate further research,
all of our code, training and evaluation sets (together
with a system demo) are published online with this
paper.1

2 Neural Language Model Architectures

The first model we apply to the dictionary-based
learning task is a recurrent neural network (RNN).
RNNs operate on variable-length sequences of in-
puts; in our case, natural language definitions, de-
scriptions or sentences. RNNs (with LSTMs) have
achieved state-of-the-art performance in language
modelling (Mikolov et al., 2010), image caption
generation (Kiros et al., 2015) and approach state-
of-the-art performance in machine translation (Bah-
danau et al., 2015).

During training, the input to the RNN is a dic-
tionary definition or sentence from an encyclopedia.

1 https://www.cl.cam.ac.uk/˜fh295/

The objective of the model is to map these defin-
ing phrases or sentences to an embedding of the
word that the definition defines. The target word
embeddings are learned independently of the RNN
weights, using the Word2Vec software (Mikolov et
al., 2013).

The set of all words in the training data consti-
tutes the vocabulary of the RNN. For each word in
this vocabulary we randomly initialise a real-valued
vector (input embedding) of model parameters. The
RNN ‘reads’ the first word in the input by applying
a non-linear projection of its embedding v1 parame-
terised by input weight matrix W and b, a vector of
biases.

A1 = φ(Wv1 + b)

yielding the first internal activation state A1. In our
implementation, we use φ(x) = tanh(x), though in
theory φ can be any differentiable non-linear func-
tion. Subsequent internal activations (after time-step
t) are computed by projecting the embedding of the
tth word and using this information to ‘update’ the
internal activation state.

At = φ(UAt−1 + Wvt + b).

As such, the values of the final internal activation
state units AN are a weighted function of all input
word embeddings, and constitute a ‘summary’ of the
information in the sentence.

2.1 Long Short Term Memory
A known limitation when training RNNs to read lan-
guage using gradient descent is that the error sig-
nal (gradient) on the training examples either van-
ishes or explodes as the number of time steps (sen-
tence length) increases (Bengio et al., 1994). Conse-
quently, after reading longer sentences the final in-
ternal activation AN typically retains useful infor-
mation about the most recently read (sentence-final)
words, but can neglect important information near
the start of the input sentence. LSTMs (Hochreiter
and Schmidhuber, 1997) were designed to mitigate
this long-term dependency problem.

At each time step t, in place of the single inter-
nal layer of units A, the LSTM RNN computes six
internal layers iw,gi,gf,go,h and m. The first, gw,
represents the core information passed to the LSTM

18

https://www.cl.cam.ac.uk/~fh295/


unit by the latest input word at t. It is computed as
a simple linear projection of the input embedding
vt (by input weights Ww) and the output state of
the LSTM at the previous time step ht−1 (by update
weights Uw):

iwt = Wwvt + Uwht−1 + bw

The layers gi,gf and go are computed as weighted
sigmoid functions of the input embeddings, again
parameterised by layer-specific weight matrices W
and U:

gst =
1

1 + exp(−(Wsvt + Usht−1 + bs))
where s stands for one of i,f or o. These vectors
take values on [0,1] and are often referred to as gat-
ing activations. Finally, the internal memory state,
mt and new output state ht, of the LSTM at t are
computed as

mt =i
w
t �git + mt−1 �g

f
t

ht =g
o
t �φ(mt),

where � indicates elementwise vector multiplica-
tion and φ is, as before, some non-linear function
(we use tanh). Thus, gi determines to what extent
the new input word is considered at each time step,
gf determines to what extent the existing state of
the internal memory is retained or forgotten in com-
puting the new internal memory, and go determines
how much this memory is considered when comput-
ing the output state at t.

The sentence-final memory state of the LSTM,
mN , a ‘summary’ of all the information in the sen-
tence, is then projected via an extra non-linear pro-
jection (parameterised by a further weight matrix)
to a target embedding space. This layer enables the
target (defined) word embedding space to take a dif-
ferent dimension to the activation layers of the RNN,
and in principle enables a more complex definition-
reading function to be learned.

2.2 Bag-of-Words NLMs
We implement a simpler linear bag-of-words (BOW)
architecture for encoding the definition phrases. As
with the RNN, this architecture learns an embedding
vi for each word in the model vocabulary, together
with a single matrix of input projection weights W .

The BOW model simply maps an input definition
with word embeddings v1 . . .vn to the sum of the
projected embeddings

∑n
i=1 Wvi. This model can

also be considered a special case of an RNN in
which the update function U and nonlinearity φ are
both the identity, so that ‘reading’ the next word in
the input phrase updates the current representation
more simply:

At = At−1 + Wvt.

2.3 Pre-trained Input Representations

We experiment with variants of these models in
which the input definition embeddings are pre-
learned and fixed (rather than randomly-initialised
and updated) during training. There are several po-
tential advantages to taking this approach. First, the
word embeddings are trained on massive corpora
and may therefore introduce additional linguistic or
conceptual knowledge to the models. Second, at test
time, the models will have a larger effective vocab-
ulary, since the pre-trained word embeddings typi-
cally span a larger vocabulary than the union of all
dictionary definitions used to train the model. Fi-
nally, the models will then map to and from the same
space of embeddings (the embedding space will be
closed under the operation of the model), so con-
ceivably could be more easily applied as a general-
purpose ‘composition engine’.

2.4 Training Objective

We train all neural language models M to map the
input definition phrase sc defining word c to a lo-
cation close to the the pre-trained embedding vc of
c. We experiment with two different cost functions
for the word-phrase pair (c,sc) from the training
data. The first is simply the cosine distance between
M(sc) and vc. The second is the rank loss

max(0,m− cos(M(sc),vc)−cos(M(sc),vr))

where vr is the embedding of a randomly-selected
word from the vocabulary other than c. This loss
function was used for language models, for example,
in (Huang et al., 2012). In all experiments we apply
a margin m = 0.1, which has been shown to work
well on word-retrieval tasks (Bordes et al., 2015).

19



2.5 Implementation Details

Since training on the dictionary data took 6-10
hours, we did not conduct a hyper-parameter search
on any validation sets over the space of possible
model configurations such as embedding dimension,
or size of hidden layers. Instead, we chose these
parameters to be as standard as possible based on
previous research. For fair comparison, any aspects
of model design that are not specific to a particu-
lar class of model were kept constant across experi-
ments.

The pre-trained word embeddings used in all of
our models (either as input or target) were learned
by a continuous bag-of-words (CBOW) model using
the Word2Vec software on approximately 8 billion
words of running text.2 When training such models
on massive corpora, a large embedding length of up
to 700 have been shown to yield best performance
(see e.g. (Faruqui et al., 2014)). The pre-trained em-
beddings used in our models were of length 500,
as a compromise between quality and memory con-
straints.

In cases where the word embeddings are learned
during training on the dictionary objective, we make
these embeddings shorter (256), since they must
be learned from much less language data. In the
RNN models, and at each time step each of the
four LSTM RNN internal layers (gating and activa-
tion states) had length 512 – another standard choice
(see e.g. (Cho et al., 2014)). The final hidden state
was mapped linearly to length 500, the dimension
of the target embedding. In the BOW models, the
projection matrix projects input embeddings (either
learned, of length 256, or pre-trained, of length 500)
to length 500 for summing.

All models were implemented with
Theano (Bergstra et al., 2010) and trained with
minibatch SGD on GPUs. The batch size was
fixed at 16 and the learning rate was controlled by
adadelta (Zeiler, 2012).

2The Word2Vec embedding models are well known; further
details can be found at https://code.google.com/p/
word2vec/ The training data for this pre-training was com-
piled from various online text sources using the script demo-
train-big-model-v1.sh from the same page.

3 Reverse Dictionaries

The most immediate application of our trained mod-
els is as a reverse dictionary or concept finder. It
is simple to look up a definition in a dictionary
given a word, but professional writers often also re-
quire suitable words for a given idea, concept or
definition.3 Reverse dictionaries satisfy this need
by returning candidate words given a phrase, de-
scription or definition. For instance, when queried
with the phrase an activity that requires strength
and determination, the OneLook.com reverse dictio-
nary returns the concepts exercise and work. Our
trained RNN model can perform a similar func-
tion, simply by mapping a phrase to a point in the
target (Word2Vec) embedding space, and returning
the words corresponding to the embeddings that are
closest to that point.

Several other academic studies have proposed
reverse dictionary models. These generally rely
on common techniques from information retrieval,
comparing definitions in their internal database to
the input query, and returning the word whose def-
inition is ‘closest’ to that query (Bilac et al., 2003;
Bilac et al., 2004; Zock and Bilac, 2004). Proxim-
ity is quantified differently in each case, but is gen-
erally a function of hand-engineered features of the
two sentences. For instance, Shaw et al. (2013) pro-
pose a method in which the candidates for a given
input query are all words in the model’s database
whose definitions contain one or more words from
the query. This candidate list is then ranked accord-
ing to a query-definition similarity metric based on
the hypernym and hyponym relations in WordNet,
features commonly used in IR such as tf-idf and a
parser.

There are, in addition, at least two commercial
online reverse dictionary applications, whose ar-
chitecture is proprietary knowledge. The first is
the Dictionary.com reverse dictionary 4, which re-
trieves candidate words from the Dictionary.com
dictionary based on user definitions or descrip-
tions. The second is OneLook.com, whose algo-
rithm searches 1061 indexed dictionaries, including

3See the testimony from professional writers at http://
www.onelook.com/?c=awards

4Available at http://dictionary.reference.
com/reverse/

20

https://code.google.com/p/word2vec/
https://code.google.com/p/word2vec/
http://www.onelook.com/?c=awards
http://www.onelook.com/?c=awards
http://dictionary.reference.com/reverse/
http://dictionary.reference.com/reverse/


all major freely-available online dictionaries and re-
sources such as Wikipedia and WordNet.

3.1 Data Collection and Training

To compile a bank of dictionary definitions for train-
ing the model, we started with all words in the tar-
get embedding space. For each of these words, we
extracted dictionary-style definitions from five elec-
tronic resources: Wordnet, The American Heritage
Dictionary, The Collaborative International Dictio-
nary of English, Wiktionary and Webster’s. We
chose these five dictionaries because they are freely-
available via the WordNik API,5 but in theory any
dictionary could be chosen. Most words in our train-
ing data had multiple definitions. For each word
w with definitions {d1 . . .dn} we included all pairs
(w,d1) . . .(w,dn) as training examples.

To allow models access to more factual knowl-
edge than might be present in a dictionary (for in-
stance, information about specific entities, places or
people, we supplemented this training data with in-
formation extracted from Simple Wikipedia. 6 For
every word in the model’s target embedding space
that is also the title of a Wikipedia article, we treat
the sentences in the first paragraph of the article as
if they were (independent) definitions of that word.
When a word in Wikipedia also occurs in one (or
more) of the five training dictionaries, we simply
add these pseudo-definitions to the training set of
definitions for the word. Combining Wikipedia and
dictionaries in this way resulted in ≈ 900,000 word-
’definition’ pairs of ≈ 100,000 unique words.

To explore the effect of the quantity of training
data on the performance of the models, we also
trained models on subsets of this data. The first sub-
set comprised only definitions from Wordnet (ap-
proximately 150,000 definitions of 75,000 words).
The second subset comprised only words in Word-
net and their first definitions (approximately 75,000
word, definition pairs).7. For all variants of RNN
and BOW models, however, reducing the training
data in this way resulted in a clear reduction in per-

5See http://developer.wordnik.com
6https://simple.wikipedia.org/wiki/Main_

Page
7As with other dictionaries, the first definition in WordNet

generally corresponds to the most typical or common sense of a
word.

formance on all tasks. For brevity, we therefore do
not present these results in what follows.

3.2 Comparisons
As a baseline, we also implemented two entirely
unsupervised methods using the neural (Word2Vec)
word embeddings from the target word space. In the
first (W2V add), we compose the embeddings for
each word in the input query by pointwise addition,
and return as candidates the nearest word embed-
dings to the resulting composed vector.8 The sec-
ond baseline, (W2V mult), is identical except that
the embeddings are composed by elementwise mul-
tiplication. Both methods are established ways of
building phrase representations from word embed-
dings (Mitchell and Lapata, 2010).

None of the models or evaluations from previous
academic research on reverse dictionaries is pub-
licly available, so direct comparison is not possi-
ble. However, we do compare performance with
the commercial systems. The Dictionary.com sys-
tem returned no candidates for over 96% of our in-
put definitions. We therefore conduct detailed com-
parison with OneLook.com, which is the first re-
verse dictionary tool returned by a Google search
and seems to be the most popular among writers.

3.3 Reverse Dictionary Evaluation
To our knowledge there are no established means of
measuring reverse dictionary performance. In the
only previous academic research on English reverse
dictionaries that we are aware of, evaluation was
conducted on 300 word-definition pairs written by
lexicographers (Shaw et al., 2013). Since these are
not publicly available we developed new evaluation
sets and make them freely available for future eval-
uations.

The evaluation items are of three types, designed
to test different properties of the models. To cre-
ate the seen evaluation, we randomly selected 500
words from the WordNet training data (seen by all
models), and then randomly selected a definition for
each word. Testing models on the resulting 500
word-definition pairs assesses their ability to recall
or decode previously encoded information. For the

8Since we retrieve all answers from embedding spaces by
cosine similarity, addition of word embeddings is equivalent to
taking the mean.

21

http://developer.wordnik.com
https://simple.wikipedia.org/wiki/Main_Page
https://simple.wikipedia.org/wiki/Main_Page


Dictionary definitions
Test Set Seen (500 WN defs) Unseen (500 WN defs) Concept descriptions (200)

Unsup. W2V add - - - 923 .04/.16 163 339 .07/.30 150
models W2V mult - - - 1000 .00/.00 10* 1000 .00/.00 27*

OneLook 0 .89/.91 67 - - - 18.5 .38/.58 153
RNN cosine 12 .48/.73 103 22 .41/.70 116 69 .28/.54 157

RNN w2v cosine 19 .44/.70 111 19 .44/.69 126 26 .38/.66 111
RNN ranking 18 .45/.67 128 24 .43/.69 103 25 .34/.66 102

NLMs RNN w2v ranking 54 .32/.56 155 33 .36/.65 137 30 .33/.69 77
BOW cosine 22 .44/.65 129 19 .43/.69 103 50 .34/.60 99

BOW w2v cosine 15 .46/.71 124 14 .46/ .71 104 28 .36/.66 99
BOW ranking 17 .45/.68 115 22 .42/.70 95 32 .35/.69 101

BOW w2v rankng 55 .32/.56 155 36 .35/.66 138 38 .33/.72 85

median rank accuracy@10/100 rank variance

Table 1: Performance of different reverse dictionary models in different evaluation settings. *Low variance in mult
models is due to consistently poor scores, so not highlighted.

unseen evaluation, we randomly selected 500 words
from WordNet and excluded all definitions of these
words from the training data of all models.

Finally, for a fair comparison with OneLook,
which has both the seen and unseen pairs in its in-
ternal database, we built a new dataset of concept
descriptions that do not appear in the training data
for any model. To do so, we randomly selected 200
adjectives, nouns or verbs from among the top 3000
most frequent tokens in the British National Cor-
pus (Leech et al., 1994) (but outside the top 100).
We then asked ten native English speakers to write
a single-sentence ‘description’ of these words. To
ensure the resulting descriptions were good qual-
ity, for each description we asked two participants
who did not produce that description to list any
words that fitted the description (up to a maximum
of three). If the target word was not produced by
one of the two checkers, the original participant was
asked to re-write the description until the validation
was passed.9 These concept descriptions, together
with other evaluation sets, can be downloaded from
our website for future comparisons.

Given a test description, definition, or question,
all models produce a ranking of possible word an-
swers based on the proximity of their representations
of the input phrase and all possible output words.
To quantify the quality of a given ranking, we re-
port three statistics: the median rank of the correct

9Re-writing was required in 6 of the 200 cases.

Test set Word Description
Dictionary valve ”control consisting of a mechanical
definition device for controlling fluid flow”

Concept prefer ”when you like one thing
description more than another thing”

Table 2: Style difference between dictionary definitions
and concept descriptions in the evaluation.

answer (over the whole test set, lower better), the
proportion of training cases in which the correct an-
swer appears in the top 10/100 in this ranking (accu-
racy@10/100 - higher better) and the variance of the
rank of the correct answer across the test set (rank
variance - lower better).

3.4 Results

Table 1 shows the performance of the different mod-
els in the three evaluation settings. Of the unsu-
pervised composition models, elementwise addition
is clearly more effective than multiplication, which
almost never returns the correct word as the near-
est neighbour of the composition. Overall, however,
the supervised models (RNN, BOW and OneLook)
clearly outperform these baselines.

The results indicate interesting differences be-
tween the NLMs and the OneLook dictionary search
engine. The Seen (WN first) definitions in Table 1
occur in both the training data for the NLMs and
the lookup data for the OneLook model. Clearly the
OneLook algorithm is better than NLMs at retriev-

22



ing already available information (returning 89% of
correct words among the top-ten candidates on this
set). However, this is likely to come at the cost of a
greater memory footprint, since the model requires
access to its database of dictionaries at query time.10

The performance of the NLM embedding models
on the (unseen) concept descriptions task shows that
these models can generalise well to novel, unseen
queries. While the median rank for OneLook on
this evaluation is lower, the NLMs retrieve the cor-
rect answer in the top ten candidates approximately
as frequently, within the top 100 candidates more
frequently and with lower variance in ranking over
the test set. Thus, NLMs seem to generalise more
‘consistenly’ than OneLook on this dataset, in that
they generally assign a reasonably high ranking to
the correct word. In contrast, as can also be verified
by querying our we demo, OneLook tends to per-
form either very well or poorly on a given query.11

When comparing between NLMs, perhaps the
most striking observation is that the RNN models
do not significantly outperform the BOW models,
even though the BOW model output is invariant to
changes in the order of words in the definition. Users
of the online demo can verify that the BOW models
recover concepts from descriptions strikingly well,
even when the words in the description are per-
muted. This observation underlines the importance
of lexical semantics in the interpretation of language
by NLMs, and is consistent with some other recent
work on embedding sentences (Iyyer et al., 2015).

It is difficult to observe clear trends in the dif-
ferences between NLMs that learn input word em-
beddings and those with pre-trained (Word2Vec) in-
put embeddings. Both types of input yield good
performance in some situations and weaker perfor-
mance in others. In general, pre-training input em-
beddings seems to help most on the concept de-
scriptions, which are furthest from the training data
in terms of linguistic style. This is perhaps unsur-
prising, since models that learn input embeddings
from the dictionary data acquire all of their concep-

10The trained neural language models are approximately half
the size of the six training dictionaries stored as plain text, so
would be hundreds of times smaller than the OneLook database
of 1061 dictionaries if stored this way.

11We also observed that the mean ranking for NLMs was
lower than for OneLook on the concept descriptions task.

tual knowledge from this data (and thus may over-
fit to this setting), whereas models with pre-trained
embeddings have some semantic memory acquired
from general running-text language data and other
knowledge acquired from the dictionaries.

3.5 Qualitative Analysis

Some example output from the various models is
presented in Table 3. The differences illustrated
here are also evident from querying the web demo.
The first example shows how the NLMs (BOW and
RNN) generalise beyond their training data. Four
of the top five responses could be classed as ap-
propriate in that they refer to inhabitants of cold
countries. However, inspecting the WordNik train-
ing data, there is no mention of cold or anything to
do with climate in the definitions of Eskimo, Scandi-
navian, Scandinavia etc. Therefore, the embedding
models must have learned that coldness is a char-
acteristic of Scandinavia, Siberia, Russia, relates to
Eskimos etc. via connections with other concepts
that are described or defined as cold. In contrast,
the candidates produced by the OneLook and (unsu-
pervised) W2V baseline models have nothing to do
with coldness.

The second example demonstrates how the NLMs
generally return candidates whose linguistic or con-
ceptual function is appropriate to the query. For a
query referring explicitly to a means, method or pro-
cess, the RNN and BOW models produce verbs in
different forms or an appropriate deverbal noun. In
contrast, OneLook returns words of all types (aero-
dynamics, draught) that are arbitrarily related to the
words in the query. A similar effect is apparent in
the third example. While the candidates produced
by the OneLook model are the correct part of speech
(Noun), and related to the query topic, they are not
semantically appropriate. The dictionary embedding
models are the only ones that return a list of plausi-
ble habits, the class of noun requested by the input.

3.6 Cross-Lingual Reverse Dictionaries

We now show how the RNN architecture can be eas-
ily modified to create a bilingual reverse dictionary
- a system that returns candidate words in one lan-
guage given a description or definition in another.
A bilingual reverse dictionary could have clear ap-
plications for translators or transcribers. Indeed, the

23



Input
Description OneLook W2V add RNN BOW

”a native of 1:country 2:citizen 1:a 2.the 1:eskimo 2:scandinavian 1:frigid 2:cold
a cold 3:foreign 4:naturalize 3:another 4:of 3:arctic 4:indian 3:icy 4:russian

country” 5:cisco 5:whole 5:siberian 5:indian

”a way of 1:drag 2:whiz 1:the 2:through 1:glide 2:scooting 1:flying 2:gliding
moving 3:aerodynamics 4:draught 3:a 4:moving 3:glides 4:gliding 3:glide 4:fly
through 5:coefficient of drag 5:in 5:flight 5:scooting
the air”

”a habit that 1:sisterinlaw 2:fatherinlaw 1:annoy 2:your 1:bossiness 2:jealousy 1:infidelity 2:bossiness
might annoy 3:motherinlaw 4:stepson 3:might 4:that 3:annoyance 4:rudeness 3:foible 4:unfaithfulness
your spouse” 5:stepchild 5:either 5:boorishness 5:adulterous

Table 3: The top-five candidates for example queries (invented by the authors) from different reverse dictionary mod-
els. Both the RNN and BOW models are without Word2Vec input and use the cosine loss.

Input description RNN EN-FR W2V add RNN + Google
”an emotion that you might feel triste, pitoyable insister, effectivement sentiment, regretter

after being rejected” répugnante, épouvantable pourquoi, nous peur, aversion

”a small black flying insect that mouche, canard attentivement, pouvions voler, faucon
transmits disease and likes horses” hirondelle, pigeon pourrons, naturellement mouches, volant

Table 4: Responses from cross-lingual reverse dictionary models to selected queries. Underlined responses are ‘cor-
rect’ or potentially useful for a native French speaker.

problem of attaching appropriate words to concepts
may be more common when searching for words in
a second language than in a monolingual context.

To create the bilingual variant, we simply replace
the Word2Vec target embeddings with those from
a bilingual embedding space. Bilingual embedding
models use bilingual corpora to learn a space of rep-
resentations of the words in two languages, such
that words from either language that have similar
meanings are close together (Hermann and Blun-
som, 2013; Chandar et al., 2014; Gouws et al.,
2014). For a test-of-concept experiment, we used
English-French embeddings learned by the state-of-
the-art BilBOWA model (Gouws et al., 2014) from
the Wikipedia (monolingual) and Europarl (bilin-
gual) corpora.12 We trained the RNN model to map
from English definitions to English words in the
bilingual space. At test time, after reading an En-
glish definition, we then simply return the nearest
French word neighbours to that definition.

Because no benchmarks exist for quantitative

12The approach should work with any bilingual embeddings.
We thank Stephan Gouws for doing the training.

evaluation of bilingual reverse dictionaries, we com-
pare this approach qualitatively with two alternative
methods for mapping definitions to words across
languages. The first is analogous to the W2V Add
model of the previous section: in the bilingual em-
bedding space, we first compose the embeddings of
the English words in the query definition with ele-
mentwise addition, and then return the French word
whose embedding is nearest to this vector sum. The
second uses the RNN monolingual reverse dictio-
nary model to identify an English word from an En-
glish definition, and then translates that word using
Google Translate.

Table 4 shows that the RNN model can be ef-
fectively modified to create a cross-lingual reverse
dictionary. It is perhaps unsurprising that the W2V
Add model candidates are generally the lowest in
quality given the performance of the method in the
monolingual setting. In comparing the two RNN-
based methods, the RNN (embedding space) model
appears to have two advantages over the RNN +
Google approach. First, it does not require on-
line access to a bilingual word-word mapping as

24



defined e.g. by Google Translate. Second, it less
prone to errors caused by word sense ambiguity.
For example, in response to the query an emotion
you feel after being rejected, the bilingual embed-
ding RNN returns emotions or adjectives describing
mental states. In contrast, the monolingual+Google
model incorrectly maps the plausible English re-
sponse regret to the verbal infinitive regretter. The
model makes the same error when responding to a
description of a fly, returning the verb voler (to fly).

3.7 Discussion

We have shown that simply training RNN or BOW
NLMs on six dictionaries yields a reverse dictionary
that performs comparably to the leading commer-
cial system, even with access to much less dictio-
nary data. Indeed, the embedding models consis-
tently return syntactically and semantically plausi-
ble responses, which are generally part of a more
coherent and homogeneous set of candidates than
those produced by the commercial systems. We also
showed how the architecture can be easily extended
to produce bilingual versions of the same model.

In the analyses performed thus far, we only test
the dictionary embedding approach on tasks that it
was trained to accomplish (mapping definitions or
descriptions to words). In the next section, we ex-
plore whether the knowledge learned by dictionary
embedding models can be effectively transferred to
a novel task.

4 General Knowledge (crossword)
Question Answering

The automatic answering of questions posed in nat-
ural language is a central problem of Artificial In-
telligence. Although web search and IR techniques
provide a means to find sites or documents related to
language queries, at present, internet users requiring
a specific fact must still sift through pages to locate
the desired information.

Systems that attempt to overcome this, via
fully open-domain or general knowledge question-
answering (open QA), generally require large teams
of researchers, modular design and powerful infras-
tructure, exemplified by IBM’s Watson (Ferrucci
et al., 2010). For this reason, much academic re-
search focuses on settings in which the scope of the

task is reduced. This has been achieved by restrict-
ing questions to a specific topic or domain (Mollá
and Vicedo, 2007), allowing systems access to pre-
specified passages of text from which the answer can
be inferred (Iyyer et al., 2014; Weston et al., 2015),
or centering both questions and answers on a partic-
ular knowledge base (Berant and Liang, 2014; Bor-
des et al., 2014).

In what follows, we show that the dictionary em-
bedding models introduced in the previous sections
may form a useful component of an open QA sys-
tem. Given the absence of a knowledge base or
web-scale information in our architecture, we nar-
row the scope of the task by focusing on general
knowledge crossword questions. General knowl-
edge (non-cryptic, or quick) crosswords appear in
national newspapers in many countries. Crossword
question answering is more tractable than general
open QA for two reasons. First, models know the
length of the correct answer (in letters), reducing
the search space. Second, some crossword questions
mirror definitions, in that they refer to fundamental
properties of concepts (a twelve-sided shape) or re-
quest a category member (a city in Egypt).13

4.1 Evaluation

General Knowledge crossword questions come in
different styles and forms. We used the Eddie James
crossword website to compile a bank of sentence-
like general-knowledge questions.14 Eddie James is
one of the UK’s leading crossword compilers, work-
ing for several national newspapers. Our long ques-
tion set consists of the first 150 questions (starting
from puzzle #1) from his general-knowledge cross-
words, excluding clues of fewer than four words
and those whose answer was not a single word (e.g.
kingjames).

To evaluate models on a different type of clue, we
also compiled a set of shorter questions based on
the Guardian Quick Crossword. Guardian questions
still require general factual or linguistic knowledge,
but are generally shorter and somewhat more cryptic
than the longer Eddie James clues. We again formed

13As our interest is in the language understanding, we
do not address the question of fitting answers into a grid,
which is the main concern of end-to-end automated crossword
solvers (Littman et al., 2002).

14http://www.eddiejames.co.uk/

25

http://www.eddiejames.co.uk/


a list of 150 questions, beginning on 1 January 2015
and excluding any questions with multiple-word an-
swers. For clear contrast, we excluded those few
questions of length greater than four words. Of these
150 clues, a subset of 30 were single-word clues.
All evaluation datasets are available online with the
paper.

As with the reverse dictionary experiments, can-
didates are extracted from models by inputting def-
initions and returning words corresponding to the
closest embeddings in the target space. In this case,
however, we only consider candidate words whose
length matches the length specified in the clue.

Test set Word Description
Long Baudelaire ”French poet
(150) and key figure

in the development
of Symbolism.”

Short (120) satanist ”devil devotee”

Single-Word (30) guilt ”culpability”

Table 5: Examples of the different question types in the
crossword question evaluation dataset.

4.2 Benchmarks and Comparisons
As with the reverse dictionary experiments, we com-
pare RNN and BOW NLMs with a simple unsuper-
vised baseline of elementwise addition of Word2Vec
vectors in the embedding space (we discard the in-
effective W2V mult baseline), again restricting can-
didates to words of the pre-specified length. We
also compare to two bespoke online crossword-
solving engines. The first, One Across (http:
//www.oneacross.com/) is the candidate gen-
eration module of the award-winning Proverb cross-
word system (Littman et al., 2002). Proverb, which
was produced by academic researchers, has featured
in national media such as New Scientist, and beaten
expert humans in crossword solving tournaments.
The second comparison is with Crossword Maestro
(http://www.crosswordmaestro.com/), a
commercial crossword solving system that handles
both cryptic and non-cryptic crossword clues (we
focus only on the non-cryptic setting), and has also
been featured in national media.15 We are unable

15 See e.g. http://www.theguardian.com/
crosswords/crossword-blog/2012/mar/08/

to compare against a third well-known automatic
crossword solver, Dr Fill (Ginsberg, 2011), because
code for Dr Fill’s candidate-generation module is
not readily available. As with the RNN and base-
line models, when evaluating existing systems we
discard candidates whose length does not match the
length specified in the clue.

Certain principles connect the design of the ex-
isting commercial systems and differentiate them
from our approach. Unlike the NLMs, they each re-
quire query-time access to large databases contain-
ing common crossword clues, dictionary definitions,
the frequency with which words typically appear
as crossword solutions and other hand-engineered
and task-specific components (Littman et al., 2002;
Ginsberg, 2011).

4.3 Results
The performance of models on the various question
types is presented in Table 6. When evaluating the
two commercial systems, One Across and Cross-
word Maestro, we have access to web interfaces that
return up to approximately 100 candidates for each
query, so can only reliably record membership of the
top ten (accuracy@10).

On the long questions, we observe a clear advan-
tage for all dictionary embedding models over the
commercial systems and the simple unsupervised
baseline. Here, the best performing NLM (RNN
with Word2Vec input embeddings and ranking loss)
ranks the correct answer third on average, and in the
top-ten candidates over 60% of the time.

As the questions get shorter, the advantage of
the embedding models diminishes. Both the unsu-
pervised baseline and One Across answer the short
questions with comparable accuracy to the RNN and
BOW models. One reason for this may be the differ-
ence in form and style between the shorter clues and
the full definitions or encyclopedia sentences in the
dictionary training data. As the length of the clue de-
creases, finding the answer often reduces to generat-
ing synonyms (culpability - guilt), or category mem-
bers (tall animal - giraffe). The commercial systems
can retrieve good candidates for such clues among
their databases of entities, relationships and com-
mon crossword answers. Unsupervised Word2Vec

crossword-blog-computers-crack-cryptic-
clues

26

http://www.oneacross.com/
http://www.oneacross.com/
http://www.crosswordmaestro.com/
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues
http://www.theguardian.com/crosswords/crossword-blog/2012/mar/08/crossword-blog-computers-crack-cryptic-clues


Question Type avg rank -accuracy@10/100 - rank variance

Long (150) Short (120) Single-Word (30)
One Across .39 / .68 / .70 /
Crossword Maestro .27 / .43 / .73 /
W2V add 42 .31/.63 92 11 .50/.78 66 2 .79/.90 45
RNN cosine 15 .43/.69 108 22 .39/.67 117 72 .31/.52 187
RNN w2v cosine 4 .61/.82 60 7 .56/.79 60 12 .48/.72 116
RNN ranking 6 .58/.84 48 10 .51/.73 57 12 .48/.69 67
RNN w2v ranking 3 .62/.80 61 8 .57/.78 49 12 .48/.69 114
BOW cosine 4 .60/.82 54 7 .56/.78 51 12 .45/.72 137
BOW w2v cosine 4 .60/.83 56 7 .54/.80 48 3 .59/.79 111
BOW ranking 5 .62/.87 50 8 .58/.83 37 8 .55/.79 39
BOW w2v ranking 5 .60/.86 48 8 .56/.83 35 4 .55/.83 43

Table 6: Performance of different models on crossword questions of different length. The two commercial systems
are evaluated via their web interface so only accuracy@10 can be reported in those cases.

representations are also known to encode these sorts
of relationships (even after elementwise addition for
short sequences of words) (Mikolov et al., 2013).
This would also explain why the dictionary embed-
ding models with pre-trained (Word2Vec) input em-
beddings outperfom those with learned embeddings,
particularly for the shortest questions.

4.4 Qualitative Analysis

A better understanding of how the different models
arrive at their answers can be gained from consider-
ing specific examples, as presented in Table 7. The
first three examples show that, despite the apparently
superficial nature of its training data (definitions and
introductory sentences) embedding models can an-
swer questions that require factual knowledge about
people and places. Another notable characteristic of
these model is the consistent semantic appropriate-
ness of the candidate set. In the first case, the top
five candidates are all mountains, valleys or places in
the Alps; in the second, they are all biblical names.
In the third, the RNN model retrieves currencies, in
this case performing better than the BOW model,
which retrieves entities of various type associated
with the Netherlands. Generally speaking (as can
be observed by the web demo), the ‘smoothness’ or
consistency in candidate generation of the dictionary
embedding models is greater than that of the com-
mercial systems. Despite its simplicity, the unsuper-
vised W2V addition method is at times also surpris-
ingly effective, as shown by the fact that it returns

Joshua in its top candidates for the third query.
The final example in Table 7 illustrates the sur-

prising power of the BOW model. In the training
data there is a single definition for the correct an-
swer Schoenberg: United States composer and musi-
cal theorist (born in Austria) who developed atonal
composition. The only word common to both the
query and the definition is ’composer’ (there is no
tokenization that allows the BOW model to directly
connect atonal and atonality). Nevertheless, the
model is able to infer the necessary connections be-
tween the concepts in the query and the definition to
return Schoenberg as the top candidate.

Despite such cases, it remains an open question
whether, with more diverse training data, the world
knowledge required for full open QA (e.g. sec-
ondary facts about Schoenberg, such as his fam-
ily) could be encoded and retained as weights in a
(larger) dynamic network, or whether it will be nec-
essary to combine the RNN with an external mem-
ory that is less frequently (or never) updated. This
latter approach has begun to achieve impressive re-
sults on certain QA and entailment tasks (Bordes et
al., 2014; Graves et al., 2014; Weston et al., 2015).

5 Conclusion

Dictionaries exist in many of the world’s languages.
We have shown how these lexical resources can con-
stitute valuable data for training the latest neural lan-
guage models to interpret and represent the mean-
ing of phrases and sentences. While humans use

27



Input Description One Across Crossword Maestro BOW RNN

”Swiss mountain 1:noted 2:front 1:after 2:favor 1:Eiger 2.Crags 1:Eiger 2:Aosta
peak famed for its 3:Eiger 4:crown 3:ahead 4:along 3:Teton 4:Cerro 3:Cuneo 4:Lecco

north face (5)” 5:fount 5:being 5:Jebel 5:Tyrol

”Old Testament 1:Joshua 2:Exodus 1:devise 2:Daniel 1:Isaiah 2:Elijah 1:Joshua 2:Isaiah
successor to 3:Hebrew 4:person 3:Haggai 4: Isaiah 3:Joshua 4:Elisha 3:Gideon 4:Elijah
Moses (6)” 5:across 5:Joseph 5:Yahweh 5:Yahweh

”The former 1:Holland 2:general 1:Holland 2:ancient 1:Guilder 2:Holland 1:Guilder 2:Escudos
currency of the 3:Lesotho 3:earlier 4:onetime 3:Drenthe 4:Utrecht 3:Pesetas 4:Someren

Netherlands 5:qondam 5:Naarden 5:Florins
(7)”

”Arnold, 20th 1:surrealism 1:disharmony 1:Schoenberg 1:Mendelsohn
Century composer 2:laborparty 2:dissonance 2:Christleib 2:Williamson

pioneer of 3:tonemusics 3:bringabout 3:Stravinsky 3:Huddleston
atonality 4:introduced 4:constitute 4:Elderfield 4:Mandelbaum

(10)” 5:Schoenberg 5:triggeroff 5:Mendelsohn 5:Zimmerman

Table 7: Responses from different models to example crossword clues. In each case the model output is filtered to
exclude any candidates that are not of the same length as the correct answer. BOW and RNN models are trained
without Word2Vec input embeddings and cosine loss.

the phrasal definitions in dictionaries to better un-
derstand the meaning of words, machines can use
the words to better understand the phrases. We used
two dictionary embedding architectures - a recurrent
neural network architecture with a long-short-term
memory, and a simpler linear bag-of-words model -
to explicitly exploit this idea.

On the reverse dictionary task that mirrors its
training setting, NLMs that embed all known con-
cepts in a continuous-valued vector space perform
comparably to the best known commercial applica-
tions despite having access to many fewer defini-
tions. Moreover, they generate smoother sets of can-
didates and require no linguistic pre-processing or
task-specific engineering. We also showed how the
description-to-word objective can be used to train
models useful for other tasks. NLMs trained on the
same data can answer general-knowledge crossword
questions, and indeed outperform commercial sys-
tems on questions containing more than four words.
While our QA experiments focused on crosswords,
the results suggest that a similar embedding-based
approach may ultimately lead to improved output
from more general QA and dialog systems and in-
formation retrieval engines in general.

We make all code, training data, evaluation sets
and both of our linguistic tools publicly available on-

line for future research. In particular, we propose the
reverse dictionary task as a comparatively general-
purpose and objective way of evaluating how well
models compose lexical meaning into phrase or sen-
tence representations (whether or not they involve
training on definitions directly).

In the next stage of this research, we will ex-
plore ways to enhance the NLMs described here,
especially in the question-answering context. The
models are currently not trained on any question-
like language, and would conceivably improve on
exposure to such linguistic forms. We would also
like to understand better how BOW models can per-
form so well with no ‘awareness’ of word order,
and whether there are specific linguistic contexts in
which models like RNNs or others with the power
to encode word order are indeed necessary. Finally,
we intend to explore ways to endow the model with
richer world knowledge. This may require the in-
tegration of an external memory module, similar to
the promising approaches proposed in several recent
papers (Graves et al., 2014; Weston et al., 2015).

Acknowledgments

KC and YB acknowledge the support of the follow-
ing organizations: NSERC, Calcul Québec, Com-
pute Canada, the Canada Research Chairs and CI-

28



FAR. FH and AK were supported by Google Faculty
Research Award, and AK further by Google Euro-
pean Fellowship.

References
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben-

gio. 2015. Neural machine translation by jointly
learning to align and translate. In Proceeding of ICLR.

Yoshua Bengio, Patrice Simard, and Paolo Frasconi.
1994. Learning long-term dependencies with gradient
descent is difficult. Neural Networks, IEEE Transac-
tions on, 5(2):157–166.

Jonathan Berant and Percy Liang. 2014. Semantic pars-
ing via paraphrasing. In Proceedings of the Associa-
tion for Computational Linguistics.

James Bergstra, Olivier Breuleux, Frédéric Bastien, Pas-
cal Lamblin, Razvan Pascanu, Guillaume Desjardins,
Joseph Turian, David Warde-Farley, and Yoshua Ben-
gio. 2010. Theano: a CPU and GPU math expression
compiler. In Proceedings of the Python for Scientific
Computing Conference (SciPy).

Slaven Bilac, Timothy Baldwin, and Hozumi Tanaka.
2003. Improving dictionary accessibility by maximiz-
ing use of available knowledge. Traitement Automa-
tique des Langues, 44(2):199–224.

Slaven Bilac, Wataru Watanabe, Taiichi Hashimoto,
Takenobu Tokunaga, and Hozumi Tanaka. 2004. Dic-
tionary search based on the target word description. In
Proceedings of NLP 2014.

Antoine Bordes, Sumit Chopra, and Jason Weston. 2014.
Question answering with subgraph embeddings. Pro-
ceedings of EMNLP.

Antoine Bordes, Nicolas Usunier, Sumit Chopra, and
Jason Weston. 2015. Large-scale simple question
answering with memory networks. arXiv preprint
arXiv:1506.02075.

Sarath Chandar, Stanislas Lauly, Hugo Larochelle,
Mitesh Khapra, Balaraman Ravindran, Vikas C.
Raykar, and Amrita Saha. 2014. An autoencoder ap-
proach to learning bilingual word representations. In
Advances in Neural Information Processing Systems,
pages 1853–1861.

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gul-
cehre, Dzmitry Bahdanau, Fethi Bougares, Holger
Schwenk, and Yoshua Bengio. 2014. Learning phrase
representations using RNN encoder-decoder for statis-
tical machine translation. In Proceedings of EMNLP.

Manaal Faruqui, Jesse Dodge, Sujay K. Jauhar, Chris
Dyer, Eduard Hovy, and Noah A. Smith. 2014.
Retrofitting word vectors to semantic lexicons. Pro-
ceedings of the North American Chapter of the Asso-
ciation for Computational Linguistics.

David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James
Fan, David Gondek, Aditya A. Kalyanpur, Adam
Lally, J. William Murdock, Eric Nyberg, John Prager,
Nico Schlaefer, and Chris Welty. 2010. Building Wat-
son: An overview of the DeepQA project. In AI mag-
azine, volume 31(3), pages 59–79.

Matthew L. Ginsberg. 2011. Dr. FILL: Crosswords and
an implemented solver for singly weighted CSPs. In
Journal of Artificial Intelligence Research, pages 851–
886.

Stephan Gouws, Yoshua Bengio, and Greg Corrado.
2014. BilBOWA: Fast bilingual distributed represen-
tations without word alignments. In Proceedings of
NIPS Deep Learning Workshop.

Alex Graves, Greg Wayne, and Ivo Danihelka.
2014. Neural turing machines. arXiv preprint
arXiv:1410.5401.

Karl Moritz Hermann and Phil Blunsom. 2013. Multi-
lingual distributed representations without word align-
ment. In Proceedings of ICLR.

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long
short-term memory. Neural computation, 9(8):1735–
1780.

Eric H. Huang, Richard Socher, Christopher D. Manning,
and Andrew Y. Ng. 2012. Improving word representa-
tions via global context and multiple word prototypes.
In Proceedings of the Association for Computational
Linguistics.

Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino,
Richard Socher, and Hal Daumé III. 2014. A neu-
ral network for factoid question answering over para-
graphs. In Proceedings of EMNLP.

Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber,
and Hal Daumé III. 2015. Deep unordered compo-
sition rivals syntactic methods for text classification.
In Proceedings of the Association for Computational
Linguistics.

Ryan Kiros, Ruslan Salakhutdinov, and Richard S.
Zemel. 2015. Unifying visual-semantic embeddings
with multimodal neural language models. Transac-
tions of the Association for Computational Linguistics.
to appear.

Alexandre Klementiev, Ivan Titov, and Binod Bhattarai.
2012. Inducing crosslingual distributed representa-
tions of words. Proceedings of COLING.

Geoffrey Leech, Roger Garside, and Michael Bryant.
1994. CLAWS4: The tagging of the British National
Corpus. In Proceedings of COLING.

Michael L. Littman, Greg A. Keim, and Noam Shazeer.
2002. A probabilistic approach to solving crossword
puzzles. Artificial Intelligence, 134(1):23–55.

Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cer-
nockỳ, and Sanjeev Khudanpur. 2010. Recurrent neu-

29



ral network based language model. In Proceedings of
INTERSPEECH 2010.

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Cor-
rado, and Jeff Dean. 2013. Distributed representations
of words and phrases and their compositionality. In
Advances in Neural Information Processing Systems.

Jeff Mitchell and Mirella Lapata. 2010. Composition in
distributional models of semantics. Cognitive Science,
34(8):1388–1429.

Diego Mollá and José Luis Vicedo. 2007. Question an-
swering in restricted domains: An overview. Compu-
tational Linguistics, 33(1):41–61.

Ryan Shaw, Anindya Datta, Debra VanderMeer, and
Kaushik Dutta. 2013. Building a scalable database-
driven reverse dictionary. Knowledge and Data Engi-
neering, IEEE Transactions on, 25(3):528–540.

Ivan Vulic, Wim De Smet, and Marie-Francine Moens.
2011. Identifying word translations from comparable
corpora using latent topic models. In Proceedings of
the Association for Computational Linguistics.

Jason Weston, Antoine Bordes, Sumit Chopra, and
Tomas Mikolov. 2015. Towards AI-complete question
answering: A set of prerequisite toy tasks. In arXiv
preprint arXiv:1502.05698.

Matthew D. Zeiler. 2012. Adadelta: An adaptive learn-
ing rate method. In arXiv preprint arXiv:1212.5701.

Michael Zock and Slaven Bilac. 2004. Word lookup on
the basis of associations: From an idea to a roadmap.
In Proceedings of the ACL Workshop on Enhancing
and Using Electronic Dictionaries.

30