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| \title{On the Properties of Neural Machine Translation: Encoder--Decoder Approaches} | |
| \author{ | |
| Kyunghyun Cho~~~~~~~~~Bart van Merri\"enboer\\ | |
| Universit\'e de Montr\'eal \\ | |
| \And | |
| Dzmitry Bahdanau\thanks{\hspace{2mm}Research done while visiting Universit\'e de | |
| Montr\'eal}\\ | |
| Jacobs University, Germany \\ | |
| \AND | |
| Yoshua Bengio \\ | |
| Universit\'e de Montr\'eal, CIFAR Senior Fellow | |
| } | |
| \date{} | |
| \begin{document} | |
| \maketitle | |
| \begin{abstract} | |
| Neural machine translation is a relatively new approach to statistical | |
| machine translation based purely on neural networks. The neural machine | |
| translation models often consist of an encoder and a decoder. The encoder | |
| extracts a fixed-length representation from a variable-length input | |
| sentence, and the decoder generates a correct translation from this | |
| representation. In this paper, we focus on analyzing the properties of the | |
| neural machine translation using two models; RNN Encoder--Decoder and a | |
| newly proposed gated recursive convolutional neural network. We show that | |
| the neural machine translation performs relatively well on short sentences | |
| without unknown words, but its performance degrades rapidly as the length of | |
| the sentence and the number of unknown words increase. Furthermore, we find | |
| that the proposed gated recursive convolutional network learns a grammatical | |
| structure of a sentence automatically. | |
| \end{abstract} | |
| \section{Introduction} | |
| A new approach for statistical machine translation based purely on neural | |
| networks has recently been proposed~\cite{Kalchbrenner2012,Sutskever2014}. This | |
| new approach, which we refer to as {\it neural machine translation}, is inspired | |
| by the recent trend of deep representational learning. All the neural network | |
| models used in \cite{Kalchbrenner2012,Sutskever2014,Cho2014} consist of an | |
| encoder and a decoder. The encoder extracts a fixed-length vector representation | |
| from a variable-length input sentence, and from this representation the decoder | |
| generates a correct, variable-length target translation. | |
| The emergence of the neural machine translation is highly significant, both | |
| practically and theoretically. Neural machine translation models require only a | |
| fraction of the memory needed by traditional statistical machine translation | |
| (SMT) models. The models we trained for this paper require only 500MB of memory | |
| in total. This stands in stark contrast with existing SMT systems, which often | |
| require tens of gigabytes of memory. This makes the neural machine translation | |
| appealing in practice. Furthermore, unlike conventional translation systems, | |
| each and every component of the neural translation model is trained jointly to | |
| maximize the translation performance. | |
| As this approach is relatively new, there has not been much work on analyzing | |
| the properties and behavior of these models. For instance: What are the | |
| properties of sentences on which this approach performs better? How does the | |
| choice of source/target vocabulary affect the performance? In which cases does | |
| the neural machine translation fail? | |
| It is crucial to understand the properties and behavior of this new neural | |
| machine translation approach in order to determine future research directions. | |
| Also, understanding the weaknesses and strengths of neural machine translation | |
| might lead to better ways of integrating SMT and neural machine translation | |
| systems. | |
| In this paper, we analyze two neural machine translation models. One of them is | |
| the RNN Encoder--Decoder that was proposed recently in \cite{Cho2014}. The other | |
| model replaces the encoder in the RNN Encoder--Decoder model with a novel neural | |
| network, which we call a {\it gated recursive convolutional neural network} | |
| (grConv). We evaluate these two models on the task of translation from French to | |
| English. | |
| Our analysis shows that the performance of the neural machine translation model | |
| degrades quickly as the length of a source sentence increases. Furthermore, we | |
| find that the vocabulary size has a high impact on the translation performance. | |
| Nonetheless, qualitatively we find that the both models are able to generate | |
| correct translations most of the time. Furthermore, the newly proposed grConv | |
| model is able to learn, without supervision, a kind of syntactic structure over | |
| the source language. | |
| \section{Neural Networks for Variable-Length Sequences} | |
| In this section, we describe two types of neural networks that are able to | |
| process variable-length sequences. These are the recurrent neural network | |
| and the proposed gated recursive convolutional neural network. | |
| \subsection{Recurrent Neural Network with Gated Hidden Neurons} | |
| \label{sec:rnn_gated} | |
| \begin{figure}[ht] | |
| \centering | |
| \begin{minipage}{0.44\columnwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{rnn.pdf} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.44\columnwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{hidden_unit.pdf} | |
| \end{minipage} | |
| \medskip | |
| \begin{minipage}{0.44\columnwidth} | |
| \centering | |
| (a) | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.44\columnwidth} | |
| \centering | |
| (b) | |
| \end{minipage} | |
| \caption{The graphical illustration of (a) the recurrent | |
| neural network and (b) the hidden unit that adaptively forgets and remembers.} | |
| \label{fig:rnn_unit} | |
| \end{figure} | |
| A recurrent neural network (RNN, Fig.~\ref{fig:rnn_unit}~(a)) works on a variable-length sequence $x=(\vx_1, | |
| \vx_2, \cdots, \vx_T)$ by maintaining a hidden state $\vh$ over time. At each | |
| timestep $t$, the hidden state $\vh^{(t)}$ is updated by | |
| \begin{align*} | |
| \vh^{(t)} = f\left( \vh^{(t-1)}, \vx_t \right), | |
| \end{align*} | |
| where $f$ is an activation function. Often $f$ is as simple as performing a | |
| linear transformation on the input vectors, summing them, and applying an | |
| element-wise logistic sigmoid function. | |
| An RNN can be used effectively to learn a distribution over a variable-length | |
| sequence by learning the distribution over the next input $p(\vx_{t+1} \mid | |
| \vx_{t}, \cdots, \vx_{1})$. For instance, in the case of a sequence of | |
| $1$-of-$K$ vectors, the distribution can be learned by an RNN which has as an | |
| output | |
| \begin{align*} | |
| p(x_{t,j} = 1 \mid \vx_{t-1}, \dots, \vx_1) = \frac{\exp \left( | |
| \vw_j \vh_{\qt{t}}\right) } {\sum_{j'=1}^{K} \exp \left( \vw_{j'} | |
| \vh_{\qt{t}}\right) }, | |
| \end{align*} | |
| for all possible symbols $j=1,\dots,K$, where $\vw_j$ are the rows of a | |
| weight matrix $\mW$. This results in the joint distribution | |
| \begin{align*} | |
| p(x) = \prod_{t=1}^T p(x_t \mid x_{t-1}, \dots, x_1). | |
| \end{align*} | |
| Recently, in \cite{Cho2014} a new activation function for RNNs was proposed. | |
| The new activation function augments the usual logistic sigmoid activation | |
| function with two gating units called reset, $\vr$, and update, $\vz$, gates. | |
| Each gate depends on the previous hidden state $\vh^{(t-1)}$, and the current | |
| input $\vx_t$ controls the flow of information. This is reminiscent of long | |
| short-term memory (LSTM) units~\cite{Hochreiter1997}. For details about this | |
| unit, we refer the reader to \cite{Cho2014} and Fig.~\ref{fig:rnn_unit}~(b). For | |
| the remainder of this paper, we always use this new activation function. | |
| \subsection{Gated Recursive Convolutional Neural Network} | |
| \label{sec:grconv} | |
| \begin{figure*}[ht] | |
| \centering | |
| \begin{minipage}{0.35\textwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{rconv.pdf} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.19\textwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{rconv_gated_unit.pdf} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.17\textwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{rconv_gated_unit_ex1.pdf} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.17\textwidth} | |
| \centering | |
| \includegraphics[width=0.8\columnwidth]{rconv_gated_unit_ex2.pdf} | |
| \end{minipage} | |
| \begin{minipage}{0.35\textwidth} | |
| \centering | |
| (a) | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.19\textwidth} | |
| \centering | |
| (b) | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.17\textwidth} | |
| \centering | |
| (c) | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.17\textwidth} | |
| \centering | |
| (d) | |
| \end{minipage} | |
| \caption{The graphical illustration of (a) the recursive convolutional | |
| neural network and (b) the proposed gated unit for the | |
| recursive convolutional neural network. (c--d) The example structures that | |
| may be learned with the proposed gated unit.} | |
| \label{fig:rconv_unit} | |
| \end{figure*} | |
| Besides RNNs, another natural approach to dealing with variable-length sequences | |
| is to use a recursive convolutional neural network where the parameters at each | |
| level are shared through the whole network (see Fig.~\ref{fig:rconv_unit}~(a)). | |
| In this section, we introduce a binary convolutional neural network whose | |
| weights are recursively applied to the input sequence until it outputs a single | |
| fixed-length vector. In addition to a usual convolutional architecture, we | |
| propose to use the previously mentioned gating mechanism, which allows the | |
| recursive network to learn the structure of the source sentences on the fly. | |
| Let $x=(\vx_1, \vx_2, \cdots, \vx_T)$ be an input sequence, where $\vx_t \in | |
| \RR^d$. The proposed gated recursive convolutional neural network (grConv) | |
| consists of four weight matrices $\mW^l$, $\mW^r$, $\mG^l$ and $\mG^r$. At each | |
| recursion level $t \in \left[ 1, T-1\right]$, the activation of the $j$-th | |
| hidden unit $h^{(t)}_j$ is computed by | |
| \begin{align} | |
| \label{eq:grconv_main} | |
| h^{(t)}_j = \omega_c \tilde{h}^{(t)}_j + \omega_l h^{(t-1)}_{j-1} + \omega_r | |
| h^{(t-1)}_j, | |
| \end{align} | |
| where $\omega_c$, $\omega_l$ and $\omega_r$ are the values of a gater that sum | |
| to $1$. The hidden unit is initialized as | |
| \begin{align*} | |
| h^{(0)}_j = \mU \vx_j, | |
| \end{align*} | |
| where $\mU$ projects the input into a hidden space. | |
| The new activation $\tilde{h}^{(t)}_j$ is computed as usual: | |
| \begin{align*} | |
| \tilde{h}^{(t)}_j = \phi\left( \mW^l h^{(t)}_{j-1} + \mW^r h^{(t)}_{j} | |
| \right), | |
| \end{align*} | |
| where $\phi$ is an element-wise nonlinearity. | |
| The gating coefficients $\omega$'s are computed by | |
| \begin{align*} | |
| \left[ | |
| \begin{array}{c} | |
| \omega_c \\ | |
| \omega_l \\ | |
| \omega_r | |
| \end{array} | |
| \right] = \frac{1}{Z} | |
| \exp\left( \mG^l h^{(t)}_{j-1} + \mG^r h^{(t)}_{j} | |
| \right), | |
| \end{align*} | |
| where $\mG^l, \mG^r \in \RR^{3 \times d}$ and | |
| \[ | |
| Z = \sum_{k=1}^3 \left[\exp\left( \mG^l h^{(t)}_{j-1} + \mG^r h^{(t)}_{j} \right)\right]_k. | |
| \] | |
| According to this activation, one can think of the activation of a single node | |
| at recursion level $t$ as a choice between either a new activation computed from | |
| both left and right children, the activation from the left child, or the | |
| activation from the right child. This choice allows the overall structure of the | |
| recursive convolution to change adaptively with respect to an input sample. See | |
| Fig.~\ref{fig:rconv_unit}~(b) for an illustration. | |
| In this respect, we may even consider the proposed grConv as doing a kind of | |
| unsupervised parsing. If we consider the case where the gating unit makes a | |
| hard decision, i.e., $\omega$ follows an 1-of-K coding, it is easy to see that | |
| the network adapts to the input and forms a tree-like structure (See | |
| Fig.~\ref{fig:rconv_unit}~(c--d)). However, we leave the further investigation | |
| of the structure learned by this model for future research. | |
| \section{Purely Neural Machine Translation} | |
| \subsection{Encoder--Decoder Approach} | |
| The task of translation can be understood from the perspective of machine learning | |
| as learning the conditional distribution $p(f \mid e)$ of a target sentence | |
| (translation) $f$ given a source sentence $e$. Once the conditional distribution | |
| is learned by a model, one can use the model to directly sample a target | |
| sentence given a source sentence, either by actual sampling or by using a | |
| (approximate) search algorithm to find the maximum of the distribution. | |
| A number of recent papers have proposed to use neural networks to directly learn | |
| the conditional distribution from a bilingual, parallel | |
| corpus~\cite{Kalchbrenner2012,Cho2014,Sutskever2014}. For instance, the authors | |
| of \cite{Kalchbrenner2012} proposed an approach involving a convolutional | |
| $n$-gram model to extract a fixed-length vector of a source sentence which is | |
| decoded with an inverse convolutional $n$-gram model augmented with an RNN. In | |
| \cite{Sutskever2014}, an RNN with LSTM units was used to encode a source | |
| sentence and starting from the last hidden state, to decode a target sentence. | |
| Similarly, the authors of \cite{Cho2014} proposed to use an RNN to encode and | |
| decode a pair of source and target phrases. | |
| \begin{figure} | |
| \centering | |
| \includegraphics[width=0.9\columnwidth]{encode_decode.pdf} | |
| \caption{The encoder--decoder architecture} | |
| \label{fig:encode_decode} | |
| \end{figure} | |
| At the core of all these recent works lies an encoder--decoder architecture (see | |
| Fig.~\ref{fig:encode_decode}). The encoder processes a variable-length input | |
| (source sentence) and builds a fixed-length vector representation (denoted as | |
| $\vz$ in Fig.~\ref{fig:encode_decode}). Conditioned on the encoded | |
| representation, the decoder generates a variable-length sequence (target | |
| sentence). | |
| Before \cite{Sutskever2014} this encoder--decoder approach was used mainly as | |
| a part of the existing statistical machine translation (SMT) system. This | |
| approach was used to re-rank the $n$-best list generated by the SMT system in | |
| \cite{Kalchbrenner2012}, and the authors of \cite{Cho2014} used this approach | |
| to provide an additional score for the existing phrase table. | |
| In this paper, we concentrate on analyzing the direct translation performance, | |
| as in \cite{Sutskever2014}, with two model configurations. In both models, we | |
| use an RNN with the gated hidden unit~\cite{Cho2014}, as this is one of the only | |
| options that does not require a non-trivial way to determine the target length. | |
| The first model will use the same RNN with the gated hidden unit as an encoder, | |
| as in \cite{Cho2014}, and the second one will use the proposed gated recursive | |
| convolutional neural network (grConv). We aim to understand the inductive bias | |
| of the encoder--decoder approach on the translation performance measured by | |
| BLEU. | |
| \section{Experiment Settings} | |
| \subsection{Dataset} | |
| We evaluate the encoder--decoder models on the task of English-to-French | |
| translation. We use the bilingual, parallel corpus which is a set of 348M | |
| selected by the method in \cite{Axelrod2011} from a combination of Europarl (61M | |
| words), news commentary (5.5M), UN (421M) and two crawled corpora of 90M and | |
| 780M words respectively.\footnote{All the data can be downloaded from | |
| \url{http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/}.} We did not use | |
| separate monolingual data. The performance of the neural machien translation | |
| models was measured on the news-test2012, news-test2013 and news-test2014 sets | |
| (~3000 lines each). When comparing to the SMT system, we use news-test2012 and | |
| news-test2013 as our development set for tuning the SMT system, and | |
| news-test2014 as our test set. | |
| Among all the sentence pairs in the prepared parallel corpus, for reasons of | |
| computational efficiency we only use the pairs where both English and French | |
| sentences are at most 30 words long to train neural networks. Furthermore, we | |
| use only the 30,000 most frequent words for both English and French. All the | |
| other rare words are considered unknown and are mapped to a special token | |
| ($\left[ \text{UNK} \right]$). | |
| \subsection{Models} | |
| We train two models: The RNN Encoder--Decoder~(RNNenc)\cite{Cho2014} and the | |
| newly proposed gated recursive convolutional neural network (grConv). Note that | |
| both models use an RNN with gated hidden units as a decoder (see | |
| Sec.~\ref{sec:rnn_gated}). | |
| We use minibatch stochastic gradient descent with AdaDelta~\cite{Zeiler-2012} to | |
| train our two models. We initialize the square weight matrix (transition matrix) | |
| as an orthogonal matrix with its spectral radius set to $1$ in the case of the | |
| RNNenc and $0.4$ in the case of the grConv. $\tanh$ and a rectifier | |
| ($\max(0,x)$) are used as the element-wise nonlinear functions for the RNNenc | |
| and grConv respectively. | |
| The grConv has 2000 hidden neurons, whereas the RNNenc has 1000 hidden | |
| neurons. The word embeddings are 620-dimensional in both cases.\footnote{ | |
| In all cases, we train the whole network including the word embedding matrix. | |
| } | |
| Both models were trained for approximately 110 hours, which is equivalent to | |
| 296,144 updates and 846,322 updates for the grConv and RNNenc, | |
| respectively.\footnote{ | |
| The code will be available online, should the paper be accepted. | |
| } | |
| \begin{table*}[ht] | |
| \centering | |
| \begin{minipage}{0.48\textwidth} | |
| \centering | |
| \begin{tabular}{c | c | c c} | |
| & Model & Development & Test \\ | |
| \hline | |
| \hline | |
| \multirow{5}{*}{\rotatebox[origin=c]{90}{All}} | |
| & RNNenc & 13.15 & 13.92 \\ | |
| & grConv & 9.97 & 9.97 \\ | |
| & Moses & 30.64 & 33.30 \\ | |
| & Moses+RNNenc$^\star$ & 31.48 & 34.64 \\ | |
| & Moses+LSTM$^\circ$ & 32 & 35.65 \\ | |
| \hline | |
| \multirow{3}{*}{\rotatebox[origin=c]{90}{No UNK}} | |
| & RNNenc & 21.01 & 23.45 \\ | |
| & grConv & 17.19 & 18.22 \\ | |
| & Moses & 32.77 & 35.63 \\ | |
| \end{tabular} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.48\textwidth} | |
| \centering | |
| \begin{tabular}{c | c | c c} | |
| & Model & Development & Test \\ | |
| \hline | |
| \hline | |
| \multirow{3}{*}{\rotatebox[origin=c]{90}{All}} | |
| & RNNenc & 19.12 & 20.99 \\ | |
| & grConv & 16.60 & 17.50 \\ | |
| & Moses & 28.92 & 32.00 \\ | |
| \hline | |
| \multirow{3}{*}{\rotatebox[origin=c]{90}{No UNK}} | |
| & RNNenc & 24.73 & 27.03 \\ | |
| & grConv & 21.74 & 22.94 \\ | |
| & Moses & 32.20 & 35.40 \\ | |
| \end{tabular} | |
| \end{minipage} | |
| \begin{minipage}{0.48\textwidth} | |
| \centering | |
| (a) All Lengths | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.48\textwidth} | |
| \centering | |
| (b) 10--20 Words | |
| \end{minipage} | |
| \caption{BLEU scores computed on the development and test sets. The top | |
| three rows show the scores on all the sentences, and the bottom three | |
| rows on the sentences having no unknown words. ($\star$) The result | |
| reported in \cite{Cho2014} where the RNNenc was used to score phrase | |
| pairs in the phrase table. ($\circ$) The result reported in | |
| \cite{Sutskever2014} where an encoder--decoder with LSTM units was used | |
| to re-rank the $n$-best list generated by Moses.} | |
| \label{tab:bleu} | |
| \end{table*} | |
| \subsubsection{Translation using Beam-Search} | |
| We use a basic form of beam-search to find a translation that maximizes the | |
| conditional probability given by a specific model (in this case, either the | |
| RNNenc or the grConv). At each time step of the decoder, we keep the $s$ | |
| translation candidates with the highest log-probability, where $s=10$ is the | |
| beam-width. During the beam-search, we exclude any hypothesis that includes an | |
| unknown word. For each end-of-sequence symbol that is selected among the | |
| highest scoring candidates the beam-width is reduced by one, until the | |
| beam-width reaches zero. | |
| The beam-search to (approximately) find a sequence of maximum log-probability | |
| under RNN was proposed and used successfully in \cite{Graves2012} and | |
| \cite{Boulanger2013}. Recently, the authors of \cite{Sutskever2014} found this | |
| approach to be effective in purely neural machine translation based on LSTM | |
| units. | |
| When we use the beam-search to find the $k$ best translations, we do not use a | |
| usual log-probability but one normalized with respect to the length of the | |
| translation. This prevents the RNN decoder from favoring shorter translations, | |
| behavior which was observed earlier in, e.g.,~\cite{Graves2013}. | |
| \begin{figure*}[ht] | |
| \centering | |
| \begin{minipage}{0.31\textwidth} | |
| \centering | |
| \includegraphics[width=1.\columnwidth]{len_norm.pdf} | |
| \\ | |
| (a) RNNenc | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.31\textwidth} | |
| \centering | |
| \includegraphics[width=1.\columnwidth]{grconv_len_norm.pdf} | |
| \\ | |
| (b) grConv | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.31\textwidth} | |
| \centering | |
| \includegraphics[width=1.\columnwidth]{max_unk_norm.pdf} | |
| \\ | |
| (c) RNNenc | |
| \end{minipage} | |
| \caption{The BLEU scores achieved by (a) the RNNenc and (b) the grConv for | |
| sentences of a given length. The plot is smoothed by taking a window of size | |
| 10. (c) The BLEU scores achieved by the RNN model for sentences with less | |
| than a given number of unknown words.} | |
| \label{fig:bleu_length} | |
| \end{figure*} | |
| \begin{table*}[htp] | |
| \begin{minipage}{0.99\textwidth} | |
| \small | |
| \centering | |
| \begin{tabular}{c | p{13cm}} | |
| Source & She explained her new position of foreign affairs and security policy | |
| representative as a reply to a question: "Who is the European Union? Which phone | |
| number should I call?"; i.e. as an important step to unification and better | |
| clarity of Union's policy towards countries such as China or India. \\ | |
| \hline | |
| Reference & Elle a expliqué le nouveau poste de la Haute représentante pour les | |
| affaires étrangères et la politique de défense dans le cadre d'une réponse à la | |
| question: "Qui est qui à l'Union européenne?" "A quel numéro de téléphone | |
| dois-je appeler?", donc comme un pas important vers l'unicité et une plus grande | |
| lisibilité de la politique de l'Union face aux états, comme est la Chine ou bien | |
| l'Inde. \\ | |
| \hline | |
| RNNEnc & Elle a décrit sa position en matière de politique étrangère et de | |
| sécurité ainsi que la politique de l'Union européenne en matière de gouvernance | |
| et de démocratie . \\ | |
| \hline | |
| grConv & Elle a expliqué sa nouvelle politique étrangère et de sécurité en | |
| réponse à un certain nombre de questions : "Qu'est-ce que l'Union européenne ? " | |
| . \\ | |
| \hline | |
| Moses & Elle a expliqué son nouveau poste des affaires étrangères et la | |
| politique de sécurité représentant en réponse à une question: "Qui est l'Union | |
| européenne? Quel numéro de téléphone dois-je appeler?"; c'est comme une étape | |
| importante de l'unification et une meilleure lisibilité de la politique de | |
| l'Union à des pays comme la Chine ou l'Inde . \\ | |
| \hline | |
| \multicolumn{2}{c}{} \\ | |
| \hline | |
| Source & The investigation should be complete by the end of the year when the | |
| findings will be presented to Deutsche Bank's board of managing directors - with | |
| recommendations for action. \\ | |
| \hline | |
| Reference & L'examen doit être terminé d'ici la fin de l'année, ensuite les | |
| résultats du conseil d'administration de la Deutsche Bank doivent être présentés | |
| - avec recommandation, d' habitude. \\ | |
| \hline | |
| RNNEnc & L'étude devrait être terminée à la fin de l' année, lorsque les | |
| conclusions seront présentées au conseil d'administration de la Deutsche Bank, | |
| conseil d'association avec des mesures. \\ | |
| \hline | |
| grConv & L'enquête devrait être terminée à la fin de l'année où les conclusions | |
| seront présentées par le conseil d'administration de la BCE à la direction des | |
| recommandations. \\ | |
| \hline | |
| Moses & L'enquête devrait être terminé d'ici la fin de l'année lorsque les | |
| résultats seront présentés à la Deutsche Bank conseil des directeurs généraux - | |
| avec des recommandations . \\ | |
| \hline | |
| \multicolumn{2}{c}{} \\ | |
| \hline | |
| Source & And there are thorny mechanical questions that must be resolved during | |
| that time, like how to balance the state's mandate of "adequate access" to | |
| licensed marijuana with its prohibitions on cannabis businesses within 1,000 | |
| feet of a school, park, playground or child care center. \\ | |
| \hline | |
| Reference & Pendant ce temps, des questions pratiques restent en suspens: | |
| comment équilibrer le mandat de l'état qui garantit un accès approprié à la | |
| marijuana agréée et interdit l'installation de commerces de vente de cannabis | |
| dans un rayon de 30 km autour d'une école, d'un parc, d'un terrain de jeu ou | |
| d'une crèche. \\ | |
| \hline | |
| RNNEnc & Il y a des questions préventives qui se posent quant à l'équilibre des | |
| droits de l'enfant dans les limites d'une entreprise de collecte de sang. \\ | |
| \hline | |
| grConv & De façon générale, il y a des raisons de sécurité pour que les | |
| entreprises aient accès à des milliers de centres de pêche, d'eau ou de | |
| recherche. \\ | |
| \hline | |
| Moses & Et il y a des problèmes mécaniques complexes qui doivent être résolues | |
| au cours de cette période, comme la manière d'équilibrer le mandat de "l'accès | |
| adéquat" permis de marijuana avec l'interdiction du cannabis aux entreprises de | |
| 1000 pieds d'une école de jeu ou de parc, le service de garde. \\ | |
| \end{tabular} | |
| \\ | |
| {\normalsize (a) Long Sentences} | |
| \end{minipage} | |
| \vspace{3mm} | |
| \begin{minipage}{0.99\textwidth} | |
| \small | |
| \centering | |
| \begin{tabular}{c | p{13cm}} | |
| Source & There is still no agreement as to which election rules to follow. \\ | |
| \hline | |
| Reference & Toutefois il n'existe toujours pas d'accord selon quel règlement de | |
| vote il faut procéder. \\ | |
| \hline | |
| RNNEnc & Il n'y a pas encore d'accord sur les règles électorales. \\ | |
| \hline | |
| grConv & Il n'y a pas encore d'accord sur la question des élections à suivre. \\ | |
| \hline | |
| Moses & Il y a toujours pas d'accord sur l'élection des règles à suivre. \\ | |
| \hline | |
| \multicolumn{2}{c}{} \\ | |
| \hline | |
| Source & Many of these ideas may have been creative, but they didn't necessarily | |
| work. \\ | |
| \hline | |
| Reference & Beaucoup de ces idées étaient créatives mais elles n'ont pas | |
| forcément fonctionné. \\ | |
| \hline | |
| RNNEnc & Bon nombre de ces idées ont peut-être été créatrices, mais elles ne | |
| s'appliquaient pas nécessairement. \\ | |
| \hline | |
| grConv & Beaucoup de ces idées peuvent être créatives, mais elles n'ont pas | |
| fonctionné. \\ | |
| \hline | |
| Moses & Beaucoup de ces idées ont pu être créatif, mais ils n'ont pas | |
| nécessairement. \\ | |
| \hline | |
| \multicolumn{2}{c}{} \\ | |
| \hline | |
| Source & There is a lot of consensus between the Left and the Right on this | |
| subject. \\ | |
| \hline | |
| Reference & C'est qu'il y a sur ce sujet un assez large consensus entre gauche | |
| et droite. \\ | |
| \hline | |
| RNNEnc & Il existe beaucoup de consensus entre la gauche et le droit à la | |
| question. \\ | |
| \hline | |
| grConv & Il y a un consensus entre la gauche et le droit sur cette question. \\ | |
| \hline | |
| Moses & Il y a beaucoup de consensus entre la gauche et la droite sur ce sujet. | |
| \\ | |
| \hline | |
| \multicolumn{2}{c}{} \\ | |
| \hline | |
| Source & According to them, one can find any weapon at a low price right now. \\ | |
| \hline | |
| Reference & Selon eux, on peut trouver aujourd'hui à Moscou n'importe quelle | |
| arme pour un prix raisonnable.\\ | |
| \hline | |
| RNNEnc & Selon eux, on peut se trouver de l'arme à un prix trop bas.\\ | |
| \hline | |
| grConv & En tout cas, ils peuvent trouver une arme à un prix très bas à la | |
| fois.\\ | |
| \hline | |
| Moses & Selon eux, on trouve une arme à bas prix pour l'instant. | |
| \end{tabular} | |
| \\ | |
| {\normalsize (b) Short Sentences} | |
| \end{minipage} | |
| \caption{The sample translations along with the source sentences and the reference translations.} | |
| \label{tbl:translations} | |
| \end{table*} | |
| \section{Results and Analysis} | |
| \subsection{Quantitative Analysis} | |
| In this paper, we are interested in the properties of the neural machine | |
| translation models. Specifically, the translation quality with respect to the | |
| length of source and/or target sentences and with respect to the number of words | |
| unknown to the model in each source/target sentence. | |
| First, we look at how the BLEU score, reflecting the translation performance, | |
| changes with respect to the length of the sentences (see | |
| Fig.~\ref{fig:bleu_length} (a)--(b)). Clearly, both models perform relatively | |
| well on short sentences, but suffer significantly as the length of the | |
| sentences increases. | |
| We observe a similar trend with the number of unknown words, in | |
| Fig.~\ref{fig:bleu_length} (c). As expected, the performance degrades rapidly as | |
| the number of unknown words increases. This suggests that it will be an | |
| important challenge to increase the size of vocabularies used by the neural | |
| machine translation system in the future. Although we only present the result | |
| with the RNNenc, we observed similar behavior for the grConv as well. | |
| In Table~\ref{tab:bleu}~(a), we present the translation performances obtained | |
| using the two models along with the baseline phrase-based SMT system.\footnote{ | |
| We used Moses as a baseline, trained with additional monolingual data for a | |
| 4-gram language model. | |
| } Clearly the phrase-based SMT system still shows the superior performance over | |
| the proposed purely neural machine translation system, but we can see that under | |
| certain conditions (no unknown words in both source and reference sentences), | |
| the difference diminishes quite significantly. Furthermore, if we consider only | |
| short sentences (10--20 words per sentence), the difference further decreases | |
| (see Table~\ref{tab:bleu}~(b). | |
| Furthermore, it is possible to use the neural machine translation models | |
| together with the existing phrase-based system, which was found recently in | |
| \cite{Cho2014,Sutskever2014} to improve the overall translation performance | |
| (see Table~\ref{tab:bleu}~(a)). | |
| This analysis suggests that that the current neural translation approach has | |
| its weakness in handling long sentences. The most obvious explanatory | |
| hypothesis is that the fixed-length vector representation does not have enough | |
| capacity to encode a long sentence with complicated structure and meaning. In | |
| order to encode a variable-length sequence, a neural network may ``sacrifice'' | |
| some of the important topics in the input sentence in order to remember others. | |
| This is in stark contrast to the conventional phrase-based machine translation | |
| system~\cite{Koehn2003}. As we can see from Fig.~\ref{fig:moses_bleu_length}, | |
| the conventional system trained on the same dataset (with additional monolingual | |
| data for the language model) tends to get a higher BLEU score on longer | |
| sentences. | |
| In fact, if we limit the lengths of both the source sentence and the reference | |
| translation to be between 10 and 20 words and use only the sentences with no | |
| unknown words, the BLEU scores on the test set are 27.81 and 33.08 for the | |
| RNNenc and Moses, respectively. | |
| Note that we observed a similar trend even when we used sentences of up to 50 | |
| words to train these models. | |
| \subsection{Qualitative Analysis} | |
| Although BLEU score is used as a de-facto standard metric for evaluating the | |
| performance of a machine translation system, it is not the perfect metric~(see, | |
| e.g., \cite{Song13,Liu2011}). Hence, here we present some of the actual | |
| translations generated from the two models, RNNenc and grConv. | |
| In Table.~\ref{tbl:translations} (a)--(b), we show the translations of some | |
| randomly selected sentences from the development and test sets. We chose the | |
| ones that have no unknown words. (a) lists long sentences (longer than 30 | |
| words), and (b) short sentences (shorter than 10 words). We can see that, | |
| despite the difference in the BLEU scores, all three models (RNNenc, grConv and | |
| Moses) do a decent job at translating, especially, short sentences. When the | |
| source sentences are long, however, we notice the performance degradation of the | |
| neural machine translation models. | |
| \begin{figure}[ht] | |
| \centering | |
| \includegraphics[width=0.9\columnwidth]{moses_len_norm.pdf} \caption{The BLEU | |
| scores achieved by an SMT system for sentences of a given length. The plot is | |
| smoothed by taking a window of size 10. | |
| We use the solid, dotted and dashed | |
| lines to show the effect of different lengths of source, reference or both | |
| of them, respectively.} | |
| \label{fig:moses_bleu_length} | |
| \end{figure} | |
| \begin{figure*}[ht] | |
| \begin{minipage}{0.5\textwidth} | |
| \centering | |
| \includegraphics[width=\textwidth,clip=true,trim=90 50 90 50]{obama.pdf} | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.48\textwidth} | |
| \centering | |
| \begin{tabular}{l} | |
| Translations \\ | |
| \hline | |
| Obama est le Président des États-Unis . (2.06)\\ | |
| Obama est le président des États-Unis . (2.09)\\ | |
| Obama est le président des Etats-Unis . (2.61)\\ | |
| Obama est le Président des Etats-Unis . (3.33)\\ | |
| Barack Obama est le président des États-Unis . (4.41)\\ | |
| Barack Obama est le Président des États-Unis . (4.48)\\ | |
| Barack Obama est le président des Etats-Unis . (4.54)\\ | |
| L'Obama est le Président des États-Unis . (4.59)\\ | |
| L'Obama est le président des États-Unis . (4.67)\\ | |
| Obama est président du Congrès des États-Unis .(5.09) \\ | |
| \end{tabular} | |
| \end{minipage} | |
| \begin{minipage}{0.4\textwidth} | |
| \centering | |
| (a) | |
| \end{minipage} | |
| \hfill | |
| \begin{minipage}{0.58\textwidth} | |
| \centering | |
| (b) | |
| \end{minipage} | |
| \caption{(a) The visualization of the grConv structure when the input is | |
| {\it ``Obama is the President of the United States.''}. Only edges with | |
| gating coefficient $\omega$ higher than $0.1$ are shown. (b) The top-$10$ translations | |
| generated by the grConv. The numbers in parentheses are the negative | |
| log-probability of the translations.} | |
| \label{fig:obama} | |
| \end{figure*} | |
| Additionally, we present here what type of structure the proposed gated | |
| recursive convolutional network learns to represent. With a sample sentence | |
| {\it ``Obama is the President of the United States''}, we present the parsing | |
| structure learned by the grConv encoder and the generated translations, in | |
| Fig.~\ref{fig:obama}. The figure suggests that the grConv extracts the vector | |
| representation of the sentence by first merging {\it ``of the United States''} | |
| together with {\it ``is the President of''} and finally combining this with {\it | |
| ``Obama is''} and {\it ``.''}, which is well correlated with our intuition. | |
| Despite the lower performance the grConv showed compared | |
| to the RNN Encoder--Decoder,\footnote{ | |
| However, it should be noted that the number of gradient updates used to | |
| train the grConv was a third of that used to train the RNNenc. Longer | |
| training may change the result, but for a fair comparison we chose to | |
| compare models which were trained for an equal amount of time. Neither model | |
| was trained to convergence. | |
| } | |
| we find this property of the grConv learning a | |
| grammar structure automatically interesting and believe further investigation is | |
| needed. | |
| \section{Conclusion and Discussion} | |
| In this paper, we have investigated the property of a recently introduced family | |
| of machine translation system based purely on neural networks. We focused on | |
| evaluating an encoder--decoder approach, proposed recently in | |
| \cite{Kalchbrenner2012,Cho2014,Sutskever2014}, on the task of | |
| sentence-to-sentence translation. Among many possible encoder--decoder models we | |
| specifically chose two models that differ in the choice of the encoder; (1) RNN | |
| with gated hidden units and (2) the newly proposed gated recursive convolutional | |
| neural network. | |
| After training those two models on pairs of English and French sentences, we | |
| analyzed their performance using BLEU scores with respect to the lengths of | |
| sentences and the existence of unknown/rare words in sentences. Our analysis | |
| revealed that the performance of the neural machine translation suffers | |
| significantly from the length of sentences. However, qualitatively, we found | |
| that the both models are able to generate correct translations very well. | |
| These analyses suggest a number of future research directions in machine | |
| translation purely based on neural networks. | |
| Firstly, it is important to find a way to scale up training a neural network | |
| both in terms of computation and memory so that much larger vocabularies for | |
| both source and target languages can be used. Especially, when it comes to | |
| languages with rich morphology, we may be required to come up with a radically | |
| different approach in dealing with words. | |
| Secondly, more research is needed to prevent the neural machine translation | |
| system from underperforming with long sentences. Lastly, we need to explore | |
| different neural architectures, especially for the decoder. Despite the radical | |
| difference in the architecture between RNN and grConv which were used as an | |
| encoder, both models suffer from {\it the curse of sentence length}. This | |
| suggests that it may be due to the lack of representational power in the | |
| decoder. Further investigation and research are required. | |
| In addition to the property of a general neural machine translation system, we | |
| observed one interesting property of the proposed gated recursive convolutional | |
| neural network (grConv). The grConv was found to mimic the grammatical structure | |
| of an input sentence without any supervision on syntactic structure of language. | |
| We believe this property makes it appropriate for natural language processing | |
| applications other than machine translation. | |
| \section*{Acknowledgments} | |
| The authors would like to acknowledge the support of the following agencies for | |
| research funding and computing support: NSERC, Calcul Qu\'{e}bec, Compute Canada, | |
| the Canada Research Chairs and CIFAR. | |
| \bibliographystyle{acl} | |
| \bibliography{strings,strings-shorter,ml,aigaion,myref} | |
| \end{document} | |