Coreference Resolution Using AdapterFusion-based Multi-Task learning

Sobha Lalitha Devi, Vijay Sundar Ram R., Pattabhi RK. Rao

Abstract
End-to-end coreference resolution is the task of identifying the mentions in a text that refer to the same real world entity and grouping them into clusters. It is crucially required for natural language understanding tasks and other high-level NLP tasks. In this paper, we present an end-to-end architecture for neural coreference resolution using AdapterFusion, a new two stage learning algorithm that leverages knowledge from multiple tasks. First task is in identifying the mentions in the text and the second to determine the coreference clusters. In the first task we learn task specific parameters called adapters that encapsulate the taskspecific information and then combine the adapters in a separate knowledge composition step to identify the mentions and their clusters. We evaluated it using FIRE corpus for Malayalam and Tamil and we achieved state of art performance.
Anthology ID:
2023.icon-1.62
Volume:
Proceedings of the 20th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2023
Address:
Goa University, Goa, India
Editors:
Jyoti D. Pawar, Sobha Lalitha Devi
Venue:
ICON
SIG:
SIGLEX
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
641–645
Language:
URL:
https://aclanthology.org/2023.icon-1.62
DOI:
Bibkey:
Cite (ACL):
Sobha Lalitha Devi, Vijay Sundar Ram R., and Pattabhi RK. Rao. 2023. Coreference Resolution Using AdapterFusion-based Multi-Task learning. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 641–645, Goa University, Goa, India. NLP Association of India (NLPAI).
Cite (Informal):
Coreference Resolution Using AdapterFusion-based Multi-Task learning (Lalitha Devi et al., ICON 2023)
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