@inproceedings{khullar-etal-2020-finding,
title = "Finding The Right One and Resolving it",
author = "Khullar, Payal and
Bhattacharya, Arghya and
Shrivastava, Manish",
editor = "Fern{\'a}ndez, Raquel and
Linzen, Tal",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-1.10",
doi = "10.18653/v1/2020.conll-1.10",
pages = "132--141",
abstract = "One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is. In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments - once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks. We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution. Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.",
}
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%0 Conference Proceedings
%T Finding The Right One and Resolving it
%A Khullar, Payal
%A Bhattacharya, Arghya
%A Shrivastava, Manish
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F khullar-etal-2020-finding
%X One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is. In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments - once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks. We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution. Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.
%R 10.18653/v1/2020.conll-1.10
%U https://aclanthology.org/2020.conll-1.10
%U https://doi.org/10.18653/v1/2020.conll-1.10
%P 132-141
Markdown (Informal)
[Finding The Right One and Resolving it](https://aclanthology.org/2020.conll-1.10) (Khullar et al., CoNLL 2020)
ACL
- Payal Khullar, Arghya Bhattacharya, and Manish Shrivastava. 2020. Finding The Right One and Resolving it. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 132–141, Online. Association for Computational Linguistics.