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        <title>Toward Better Storylines with Sentence-Level Language Models</title>
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        <namePart type="given">Daphne</namePart>
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        <namePart type="given">David</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Natalie</namePart>
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            <namePart type="given">Joel</namePart>
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    <abstract>We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.</abstract>
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%0 Conference Proceedings
%T Toward Better Storylines with Sentence-Level Language Models
%A Ippolito, Daphne
%A Grangier, David
%A Eck, Douglas
%A Callison-Burch, Chris
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ippolito-etal-2020-toward
%X We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.
%R 10.18653/v1/2020.acl-main.666
%U https://aclanthology.org/2020.acl-main.666
%U https://doi.org/10.18653/v1/2020.acl-main.666
%P 7472-7478

Markdown (Informal)

[Toward Better Storylines with Sentence-Level Language Models](https://aclanthology.org/2020.acl-main.666) (Ippolito et al., ACL 2020)

• Toward Better Storylines with Sentence-Level Language Models (Ippolito et al., ACL 2020)

ACL

• Daphne Ippolito, David Grangier, Douglas Eck, and Chris Callison-Burch. 2020. Toward Better Storylines with Sentence-Level Language Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7472–7478, Online. Association for Computational Linguistics.