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        <title>Neural Lattice-to-Sequence Models for Uncertain Inputs</title>
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        <namePart type="given">Matthias</namePart>
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    <name type="personal">
        <namePart type="given">Graham</namePart>
        <namePart type="family">Neubig</namePart>
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    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="family">Niehues</namePart>
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    <name type="personal">
        <namePart type="given">Alex</namePart>
        <namePart type="family">Waibel</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
            <namePart type="family">Hwa</namePart>
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        <name type="personal">
            <namePart type="given">Sebastian</namePart>
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    <abstract>The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM’s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.</abstract>
    <identifier type="citekey">sperber-etal-2017-neural</identifier>
    <identifier type="doi">10.18653/v1/D17-1145</identifier>
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        <date>2017-09</date>
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            <start>1380</start>
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%0 Conference Proceedings
%T Neural Lattice-to-Sequence Models for Uncertain Inputs
%A Sperber, Matthias
%A Neubig, Graham
%A Niehues, Jan
%A Waibel, Alex
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F sperber-etal-2017-neural
%X The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM’s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.
%R 10.18653/v1/D17-1145
%U https://aclanthology.org/D17-1145/
%U https://doi.org/10.18653/v1/D17-1145
%P 1380-1389

Markdown (Informal)

[Neural Lattice-to-Sequence Models for Uncertain Inputs](https://aclanthology.org/D17-1145/) (Sperber et al., EMNLP 2017)

• Neural Lattice-to-Sequence Models for Uncertain Inputs (Sperber et al., EMNLP 2017)

ACL

• Matthias Sperber, Graham Neubig, Jan Niehues, and Alex Waibel. 2017. Neural Lattice-to-Sequence Models for Uncertain Inputs. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1380–1389, Copenhagen, Denmark. Association for Computational Linguistics.