Signed Coreference Resolution

Kayo Yin, Kenneth DeHaan, Malihe Alikhani


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.
Anthology ID:
2021.emnlp-main.405
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4950–4961
Language:
URL:
https://aclanthology.org/2021.emnlp-main.405
DOI:
10.18653/v1/2021.emnlp-main.405
Bibkey:
Cite (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.
Cite (Informal):
Signed Coreference Resolution (Yin et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.405.pdf
Code
 kayoyin/scr