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        <title>Coreference Resolution Using AdapterFusion-based Multi-Task learning</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Coreference Resolution Using AdapterFusion-based Multi-Task learning
%A Sobha, Lalitha Devi
%A Vijay Sundar Ram, R.
%A Pattabhi, RK. Rao
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F sobha-etal-2023-coreference
%X 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.
%U https://aclanthology.org/2023.icon-1.62
%P 641-645

Markdown (Informal)

[Coreference Resolution Using AdapterFusion-based Multi-Task learning](https://aclanthology.org/2023.icon-1.62) (Sobha et al., ICON 2023)

• Coreference Resolution Using AdapterFusion-based Multi-Task learning (Sobha et al., ICON 2023)

ACL

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