6,222 research outputs found
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of the proposed model are jointly trained to maximize the
conditional probability of a target sequence given a source sequence. The
performance of a statistical machine translation system is empirically found to
improve by using the conditional probabilities of phrase pairs computed by the
RNN Encoder-Decoder as an additional feature in the existing log-linear model.
Qualitatively, we show that the proposed model learns a semantically and
syntactically meaningful representation of linguistic phrases.Comment: EMNLP 201
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and without MT as the end goal. Specifically, we consider the case of three
languages or modalities X, Z and Y wherein we are interested in generating
sequences in Y starting from information available in X. However, there is no
parallel training data available between X and Y but, training data is
available between X & Z and Z & Y (as is often the case in many real world
applications). Z thus acts as a pivot/bridge. An obvious solution, which is
perhaps less elegant but works very well in practice is to train a two stage
model which first converts from X to Z and then from Z to Y. Instead we explore
an interlingua inspired solution which jointly learns to do the following (i)
encode X and Z to a common representation and (ii) decode Y from this common
representation. We evaluate our model on two tasks: (i) bridge transliteration
and (ii) bridge captioning. We report promising results in both these
applications and believe that this is a right step towards truly interlingua
inspired encoder decoder architectures.Comment: 10 page
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