@inproceedings{yin-etal-2021-signed,
title = "Signed Coreference Resolution",
author = "Yin, Kayo and
DeHaan, Kenneth and
Alikhani, Malihe",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.405",
doi = "10.18653/v1/2021.emnlp-main.405",
pages = "4950--4961",
abstract = "Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.",
}
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<abstract>Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.</abstract>
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%0 Conference Proceedings
%T Signed Coreference Resolution
%A Yin, Kayo
%A DeHaan, Kenneth
%A Alikhani, Malihe
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F yin-etal-2021-signed
%X Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.
%R 10.18653/v1/2021.emnlp-main.405
%U https://aclanthology.org/2021.emnlp-main.405
%U https://doi.org/10.18653/v1/2021.emnlp-main.405
%P 4950-4961
Markdown (Informal)
[Signed Coreference Resolution](https://aclanthology.org/2021.emnlp-main.405) (Yin et al., EMNLP 2021)
ACL
- Kayo Yin, Kenneth DeHaan, and Malihe Alikhani. 2021. Signed Coreference Resolution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4950–4961, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.