@inproceedings{bai-etal-2021-joint,
title = "Joint Coreference Resolution and Character Linking for Multiparty Conversation",
author = "Bai, Jiaxin and
Zhang, Hongming and
Song, Yangqiu and
Xu, Kun",
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.43",
doi = "10.18653/v1/2021.eacl-main.43",
pages = "539--548",
abstract = "Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., {``}she{''}) or normal entities (e.g., {``}that girl{''}) rather than named entities (e.g., {``}Rachel{''}) in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose C{\^{}}2, the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that C{\^{}}2 can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.",
}
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<abstract>Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., “she”) or normal entities (e.g., “that girl”) rather than named entities (e.g., “Rachel”) in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose C\², the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that C\² can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.</abstract>
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%0 Conference Proceedings
%T Joint Coreference Resolution and Character Linking for Multiparty Conversation
%A Bai, Jiaxin
%A Zhang, Hongming
%A Song, Yangqiu
%A Xu, Kun
%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 bai-etal-2021-joint
%X Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., “she”) or normal entities (e.g., “that girl”) rather than named entities (e.g., “Rachel”) in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose C\², the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that C\² can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.
%R 10.18653/v1/2021.eacl-main.43
%U https://aclanthology.org/2021.eacl-main.43
%U https://doi.org/10.18653/v1/2021.eacl-main.43
%P 539-548
Markdown (Informal)
[Joint Coreference Resolution and Character Linking for Multiparty Conversation](https://aclanthology.org/2021.eacl-main.43) (Bai et al., EACL 2021)
ACL