@inproceedings{albalak-etal-2022-rex,
title = "{D}-{REX}: Dialogue Relation Extraction with Explanations",
author = "Albalak, Alon and
Embar, Varun and
Tuan, Yi-Lin and
Getoor, Lise and
Wang, William Yang",
editor = "Liu, Bing and
Papangelis, Alexandros and
Ultes, Stefan and
Rastogi, Abhinav and
Chen, Yun-Nung and
Spithourakis, Georgios and
Nouri, Elnaz and
Shi, Weiyan",
booktitle = "Proceedings of the 4th Workshop on NLP for Conversational AI",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlp4convai-1.4",
doi = "10.18653/v1/2022.nlp4convai-1.4",
pages = "34--46",
abstract = "Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX{'}s explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7{\%}.",
}
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<abstract>Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX’s explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.</abstract>
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%0 Conference Proceedings
%T D-REX: Dialogue Relation Extraction with Explanations
%A Albalak, Alon
%A Embar, Varun
%A Tuan, Yi-Lin
%A Getoor, Lise
%A Wang, William Yang
%Y Liu, Bing
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%Y Spithourakis, Georgios
%Y Nouri, Elnaz
%Y Shi, Weiyan
%S Proceedings of the 4th Workshop on NLP for Conversational AI
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F albalak-etal-2022-rex
%X Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX’s explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.
%R 10.18653/v1/2022.nlp4convai-1.4
%U https://aclanthology.org/2022.nlp4convai-1.4
%U https://doi.org/10.18653/v1/2022.nlp4convai-1.4
%P 34-46
Markdown (Informal)
[D-REX: Dialogue Relation Extraction with Explanations](https://aclanthology.org/2022.nlp4convai-1.4) (Albalak et al., NLP4ConvAI 2022)
ACL
- Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, and William Yang Wang. 2022. D-REX: Dialogue Relation Extraction with Explanations. In Proceedings of the 4th Workshop on NLP for Conversational AI, pages 34–46, Dublin, Ireland. Association for Computational Linguistics.