@inproceedings{yu-etal-2020-dialogue,
title = "Dialogue-Based Relation Extraction",
author = "Yu, Dian and
Sun, Kai and
Cardie, Claire and
Yu, Dong",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.444",
doi = "10.18653/v1/2020.acl-main.444",
pages = "4927--4940",
abstract = "We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at \url{https://dataset.org/dialogre/}.",
}
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<abstract>We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.</abstract>
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%0 Conference Proceedings
%T Dialogue-Based Relation Extraction
%A Yu, Dian
%A Sun, Kai
%A Cardie, Claire
%A Yu, Dong
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-dialogue
%X We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.
%R 10.18653/v1/2020.acl-main.444
%U https://aclanthology.org/2020.acl-main.444
%U https://doi.org/10.18653/v1/2020.acl-main.444
%P 4927-4940
Markdown (Informal)
[Dialogue-Based Relation Extraction](https://aclanthology.org/2020.acl-main.444) (Yu et al., ACL 2020)
ACL
- Dian Yu, Kai Sun, Claire Cardie, and Dong Yu. 2020. Dialogue-Based Relation Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4927–4940, Online. Association for Computational Linguistics.