@inproceedings{li-etal-2025-sgcd,
title = "{SGCD}: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues",
author = "Li, Xiang and
Yao, Keyu and
Shen, Gang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.613/",
pages = "11430--11440",
ISBN = "979-8-89176-335-7",
abstract = "The rise of digital social media has generated a vast amount of conversational data on platforms like Twitter and Reddit, allowing users to express sentiments through multi-turn dialogues. Dialogue-level aspect-based sentiment quadruple analysis (DiaASQ) seeks to extract structured information in the form of quadruples from these dialogues. However, it encounters challenges related to cross-utterance elements and focus bias. To address these issues, we introduce the Subtask-Guided and Causal-Debiasing (SGCD) framework. This framework leverages subtask-specific features to guide the learning of token-level features, which are then adaptively combined at the utterance level to meet specific semantic requirements. The SGCD framework employs multi-granularity attention paths to enhance cross-utterance matching and dialogue structure modeling. It also incorporates structural causal graphs and inverse probability weighting to mitigate biases from speakers and thread structures. Experimental results demonstrate that SGCD outperforms state-of-the-art methods, improving semantic modeling and bias robustness. This approach provides an effective solution for structured sentiment analysis in complex dialogues."
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<abstract>The rise of digital social media has generated a vast amount of conversational data on platforms like Twitter and Reddit, allowing users to express sentiments through multi-turn dialogues. Dialogue-level aspect-based sentiment quadruple analysis (DiaASQ) seeks to extract structured information in the form of quadruples from these dialogues. However, it encounters challenges related to cross-utterance elements and focus bias. To address these issues, we introduce the Subtask-Guided and Causal-Debiasing (SGCD) framework. This framework leverages subtask-specific features to guide the learning of token-level features, which are then adaptively combined at the utterance level to meet specific semantic requirements. The SGCD framework employs multi-granularity attention paths to enhance cross-utterance matching and dialogue structure modeling. It also incorporates structural causal graphs and inverse probability weighting to mitigate biases from speakers and thread structures. Experimental results demonstrate that SGCD outperforms state-of-the-art methods, improving semantic modeling and bias robustness. This approach provides an effective solution for structured sentiment analysis in complex dialogues.</abstract>
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%0 Conference Proceedings
%T SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues
%A Li, Xiang
%A Yao, Keyu
%A Shen, Gang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-sgcd
%X The rise of digital social media has generated a vast amount of conversational data on platforms like Twitter and Reddit, allowing users to express sentiments through multi-turn dialogues. Dialogue-level aspect-based sentiment quadruple analysis (DiaASQ) seeks to extract structured information in the form of quadruples from these dialogues. However, it encounters challenges related to cross-utterance elements and focus bias. To address these issues, we introduce the Subtask-Guided and Causal-Debiasing (SGCD) framework. This framework leverages subtask-specific features to guide the learning of token-level features, which are then adaptively combined at the utterance level to meet specific semantic requirements. The SGCD framework employs multi-granularity attention paths to enhance cross-utterance matching and dialogue structure modeling. It also incorporates structural causal graphs and inverse probability weighting to mitigate biases from speakers and thread structures. Experimental results demonstrate that SGCD outperforms state-of-the-art methods, improving semantic modeling and bias robustness. This approach provides an effective solution for structured sentiment analysis in complex dialogues.
%U https://aclanthology.org/2025.findings-emnlp.613/
%P 11430-11440
Markdown (Informal)
[SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues](https://aclanthology.org/2025.findings-emnlp.613/) (Li et al., Findings 2025)
ACL