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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2025</identifier>
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%0 Conference Proceedings
%T Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
%A Fried, Daniel
%A Stern, Mitchell
%A Klein, Dan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F fried-etal-2017-improving
%X Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.
%R 10.18653/v1/P17-2025
%U https://aclanthology.org/P17-2025
%U https://doi.org/10.18653/v1/P17-2025
%P 161-166

Markdown (Informal)

[Improving Neural Parsing by Disentangling Model Combination and Reranking Effects](https://aclanthology.org/P17-2025) (Fried et al., ACL 2017)

• Improving Neural Parsing by Disentangling Model Combination and Reranking Effects (Fried et al., ACL 2017)

ACL

• Daniel Fried, Mitchell Stern, and Dan Klein. 2017. Improving Neural Parsing by Disentangling Model Combination and Reranking Effects. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 161–166, Vancouver, Canada. Association for Computational Linguistics.