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        <namePart type="given">Andrew</namePart>
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    <abstract>We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.</abstract>
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%0 Conference Proceedings
%T Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders
%A Drozdov, Andrew
%A Verga, Patrick
%A Yadav, Mohit
%A Iyyer, Mohit
%A McCallum, Andrew
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F drozdov-etal-2019-unsupervised-latent
%X We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.
%R 10.18653/v1/N19-1116
%U https://aclanthology.org/N19-1116
%U https://doi.org/10.18653/v1/N19-1116
%P 1129-1141

Markdown (Informal)

[Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders](https://aclanthology.org/N19-1116) (Drozdov et al., NAACL 2019)

• Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (Drozdov et al., NAACL 2019)

ACL

• Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, and Andrew McCallum. 2019. Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1129–1141, Minneapolis, Minnesota. Association for Computational Linguistics.