@inproceedings{aralikatte-etal-2021-ellipsis,
title = "Ellipsis Resolution as Question Answering: An Evaluation",
author = "Aralikatte, Rahul and
Lamm, Matthew and
Hardt, Daniel and
S{\o}gaard, Anders",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.68",
doi = "10.18653/v1/2021.eacl-main.68",
pages = "810--817",
abstract = "Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).",
}
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<abstract>Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).</abstract>
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%0 Conference Proceedings
%T Ellipsis Resolution as Question Answering: An Evaluation
%A Aralikatte, Rahul
%A Lamm, Matthew
%A Hardt, Daniel
%A Søgaard, Anders
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F aralikatte-etal-2021-ellipsis
%X Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).
%R 10.18653/v1/2021.eacl-main.68
%U https://aclanthology.org/2021.eacl-main.68
%U https://doi.org/10.18653/v1/2021.eacl-main.68
%P 810-817
Markdown (Informal)
[Ellipsis Resolution as Question Answering: An Evaluation](https://aclanthology.org/2021.eacl-main.68) (Aralikatte et al., EACL 2021)
ACL
- Rahul Aralikatte, Matthew Lamm, Daniel Hardt, and Anders Søgaard. 2021. Ellipsis Resolution as Question Answering: An Evaluation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 810–817, Online. Association for Computational Linguistics.