SGCD : Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues (2025)

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.

## introduction the advent of digital socialization attracts users to frequent interactive online platforms such as twitter, reddit, facebook groups, and various forum communities. they engage in sustained, multi-turn dialogues on product experiences, social events, or personal emotions. as of 2025, global social media users have reached 5 billion, accounting for more than 60% of the world population @xcite . the extensive conversational content produced on these platforms exhibits a variety of linguistic styles and rich expressions, displaying emotional transitions among speakers and utterances. this emerging trend presents challenges for computers in understanding sentiments. dialogue-level aspect-based sentiment quadruple analysis (diaasq) @xcite , derived from aspect-based sentiment analysis (absa), has garnered significant attention in sentiment understanding. diaasq targets extracting structured sentiment quadruples (target, aspect, opinion, sentiment) from complete dialogues. it faces challenges from complex dialogue structures, cross-turn utterances, fragmented information distribution, and the possibility that quadruple components may span multiple utterances or speakers (see figure 1 ). specifically, diaasq introduces two more complex settings than sentence-level absa. first, emotional elements exhibit significant cross-utterance distribution, requiring the model to perform crosslayer matching in context. as shown in figure 1 , the opinion word "lovely" mentioned by user b in the reply, and the aspect word "curved surface" need to be cross-matched to the target word "ultra" mentioned by user a. these elements distribute unevenly across utterances, with some containing no elements. second, quadruples in dialogue often concentrate on specific targets and aspects, leading to significant focus bias in prediction perspectives, which affects the model's generalization and robustness in low-resource settings. for example, in thread 1 of figure 1 , all aspects focus on the appearance of mobile phones. recently, researchers have attempted to improve performance using multi-task learning, multigranularity modeling, or context-sharing mechanisms. models like dmin @xcite , dmca @xcite , and h2dt @xcite execute semantic sharing by parallelly training multiple subtasks (e.g., target extraction and opinion detection). however, these methods typically control learning priorities by adjusting loss function weights, lacking explicit instruments to characterize feature discrepancies between subtasks, and struggling to balance modeling effects across all subtasks. besides, they often use fixedproportion multi-view feature aggregation strategies, ignoring the actual contribution of different utterances to predictions, hindering models from capturing critical information in cross-utterance structures and rendering them susceptible to dialogue structure bias. to address these problems, this study proposes a subtask-guided and causal-debiasing (sgcd) framework for collaborative learning of three subtasks: entity extraction (ent), sentiment relation classification (rel), and sentiment polarity classification (pol), while enhancing structural awareness and bias robustness. the framework relies on two core hypotheses: first, the deep semantic features required by all subtasks can form a latent semantic space, with subtasks differing in their priority on distinct subsets of the features. second, the semantic contributions of cross-utterance features to final predictions vary significantly, requiring dynamic selection and modeling at the structural level. based on these hypotheses, the sgcd framework has three key designs to improve model performance: subtask-guided feature selection: leveraging dialogue thread modeling, this module guides the learning of deep token-level features through subtask-specific guidance signals. it adaptively selects and fuses these features at the utterance level to extract diverse deep feature representations relevant to individual subtasks. ## related work aspect-based sentiment analysis (absa) has evolved from target-aspect identification @xcite to more structured extractions such as triplets (aste) @xcite and quadruplets (asqp) @xcite . to improve semantic modeling, researchers have incorporated graph structures and attention mechanisms, including gcns @xcite , transformers @xcite , and multi-granularity denoising approaches @xcite , which also inspire dialogue-level sentiment extraction. to address the problem caused by the emotional elements spread across multiple utterances, the di-aasq task targets the joint extraction of targets, aspects, opinions, and sentiments in multi-turn dialogues. mvqpn @xcite ) captures emotional flow via query propagation, dmin @xcite integrates multi-level discourse features, while dmca @xcite and h2dt @xcite enhance cross-utterance modeling through hierarchical fusion and token-level graph reasoning. however, most approaches adopt a "shared encoder + weighted loss" framework @xcite , lacking explicit semantic disentanglement across tasks. we address this by introducing task-guided represen-tation learning and dynamic feature selection to improve interpretability and generalization. the causal inference has become increasingly relevant for debiasing in nlp, offering a principled way to model confounders and causal paths @xcite . diner @xcite applies scm, ipw, and counterfactual reasoning to reduce absa bias. mcis @xcite mitigates multimodal bias via causal graph purification, and generative counterfactual augmentation improves polarity robustness. while effective in static and multimodal settings, causal modeling in multi-turn dialogues is underexplored. prior work such as cfvqa @xcite shows promise but lacks structural bias modeling for diaasq-specific variables like thread and speaker. our work bridges this gap through integrated causal and task-structured learning. ## proposed method we represent a dialogue as @xmath0, u 2 , . . . , u n }, where u i denotes the i-th utterance and n is the total number of utterances. each utterance u @xmath1, . . . , t m i } consists of a sequence of tokens, with m i indicating its length. each utterance also includes two structural metadata: a speaker label sequence @xmath2, . . . , s n } and a reply relationship record @xmath3, . . . , l n }, where l i specifies the index of the preceding utterance it replies to. our objective is to predict labels for each token across subtasks γ ∈ {ent, rel, pol}, following the sentiment quadruple prediction setup of diaasq @xcite . the overall framework of our proposed method is illustrated in figure 2 , comprising three consecutive steps. subtask-guided feature selection: guiding the learning of deep token-level features related to subtasks, and utterance-level features are obtained from the token-level through dynamic selective fusion. subtask-specific attention fusion: constructs task-specific attention aggregation paths for tokenlevel and utterance-level features, flexibly combining information from both levels to produce final representations and classification results. bias reduction via backdoor adjustment: performs backdoor adjustment on two bias paths (@xmath4. inverse probability weighting (ipw) adjusts training losses at both the dataset-wide and sample-specific levels to approximate bias-free dis-tributions. ## conclusion this study presents the sgcd framework to address the challenges of cross-utterance quadruple extraction, varying effectiveness of tokens in utterances, and non-negligible focal biases in diaasq. previous research typically relies on subtask loss weights to regulate shared deep feature learning across subtasks. in contrast, we introduce a subtask-guided feature selector to direct the modeling of deep features at the token and utterance levels shared among subtasks. we then dynamically select the features according to each utterance's need to generate utterance-level representations. for complex cross-utterance interactions, subtask-specific attention fusion flexibly aggregates token-level and utterance-level features based on subtask-specific needs, producing prediction features tailored to each subtask. finally, causal debiasing reduces interference from confounding variables (speaker and thread) at both the dataset and sample levels, enhancing the model's adaptability to complex scenarios. experimental results on chinese and english diaasq benchmark datasets fully validate the effectiveness and robustness of the proposed sgcd method.