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        <title>A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation</title>
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        <namePart type="given">Amrita</namePart>
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        <namePart type="given">Mitesh</namePart>
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    <name type="personal">
        <namePart type="given">Kyunghyun</namePart>
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            <namePart type="given">Yuji</namePart>
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    <abstract>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 &amp; Z and Z &amp; 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.</abstract>
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%0 Conference Proceedings
%T A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
%A Saha, Amrita
%A Khapra, Mitesh M.
%A Chandar, Sarath
%A Rajendran, Janarthanan
%A Cho, Kyunghyun
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F saha-etal-2016-correlational
%X 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.
%U https://aclanthology.org/C16-1011
%P 109-118

Markdown (Informal)

[A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation](https://aclanthology.org/C16-1011) (Saha et al., COLING 2016)

• A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation (Saha et al., COLING 2016)

ACL

• Amrita Saha, Mitesh M. Khapra, Sarath Chandar, Janarthanan Rajendran, and Kyunghyun Cho. 2016. A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 109–118, Osaka, Japan. The COLING 2016 Organizing Committee.