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        <title>Query-focused Scenario Construction</title>
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        <namePart type="given">Su</namePart>
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    <name type="personal">
        <namePart type="given">Katrin</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>The news coverage of events often contains not one but multiple incompatible accounts of what happened. We develop a query-based system that extracts compatible sets of events (scenarios) from such data, formulated as one-class clustering. Our system incrementally evaluates each event’s compatibility with already selected events, taking order into account. We use synthetic data consisting of article mixtures for scalable training and evaluate our model on a new human-curated dataset of scenarios about real-world news topics. Stronger neural network models and harder synthetic training settings are both important to achieve high performance, and our final scenario construction system substantially outperforms baselines based on prior work.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1273</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Query-focused Scenario Construction
%A Wang, Su
%A Durrett, Greg
%A Erk, Katrin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-etal-2019-query
%X The news coverage of events often contains not one but multiple incompatible accounts of what happened. We develop a query-based system that extracts compatible sets of events (scenarios) from such data, formulated as one-class clustering. Our system incrementally evaluates each event’s compatibility with already selected events, taking order into account. We use synthetic data consisting of article mixtures for scalable training and evaluate our model on a new human-curated dataset of scenarios about real-world news topics. Stronger neural network models and harder synthetic training settings are both important to achieve high performance, and our final scenario construction system substantially outperforms baselines based on prior work.
%R 10.18653/v1/D19-1273
%U https://aclanthology.org/D19-1273
%U https://doi.org/10.18653/v1/D19-1273
%P 2712-2722

Markdown (Informal)

[Query-focused Scenario Construction](https://aclanthology.org/D19-1273) (Wang et al., EMNLP-IJCNLP 2019)

• Query-focused Scenario Construction (Wang et al., EMNLP-IJCNLP 2019)

ACL

• Su Wang, Greg Durrett, and Katrin Erk. 2019. Query-focused Scenario Construction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2712–2722, Hong Kong, China. Association for Computational Linguistics.