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        <title>COD3S: Diverse Generation with Discrete Semantic Signatures</title>
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    <name type="personal">
        <namePart type="given">Nathaniel</namePart>
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        <namePart type="given">João</namePart>
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    <name type="personal">
        <namePart type="given">Benjamin</namePart>
        <namePart type="family">Van Durme</namePart>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition’s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.</abstract>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T COD3S: Diverse Generation with Discrete Semantic Signatures
%A Weir, Nathaniel
%A Sedoc, João
%A Van Durme, Benjamin
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F weir-etal-2020-cod3s
%X We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition’s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.
%R 10.18653/v1/2020.emnlp-main.421
%U https://aclanthology.org/2020.emnlp-main.421
%U https://doi.org/10.18653/v1/2020.emnlp-main.421
%P 5199-5211

Markdown (Informal)

[COD3S: Diverse Generation with Discrete Semantic Signatures](https://aclanthology.org/2020.emnlp-main.421) (Weir et al., EMNLP 2020)

• COD3S: Diverse Generation with Discrete Semantic Signatures (Weir et al., EMNLP 2020)

ACL

• Nathaniel Weir, João Sedoc, and Benjamin Van Durme. 2020. COD3S: Diverse Generation with Discrete Semantic Signatures. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5199–5211, Online. Association for Computational Linguistics.