Overcome Noise and Bias: Segmentation-Aided Multi-Granularity Denoising and Debiasing for Enhanced Quarduples Extraction in Dialogue (2024)

Abstract: Dialogue Aspect-based Sentiment Quadruple analysis (DiaASQ) extends ABSA to more complex real-world scenarios (i.e., dialogues), which makes existing generation methods encounter heightened noise and order bias challenges, leading to decreased robustness and accuracy.To address these, we propose the Segmentation-Aided multi-grained Denoising and Debiasing (SADD) method. For noise, we propose the Multi-Granularity Denoising Generation model (MGDG), achieving word-level denoising via sequence labeling and utterance-level denoising via topic-aware dialogue segmentation. Denoised Attention in MGDG integrates multi-grained denoising information to help generate denoised output.For order bias, we first theoretically analyze its direct cause as the gap between ideal and actual training objectives and propose a distribution-based solution. Since this solution introduces a one-to-many learning challenge, our proposed Segmentation-aided Order Bias Mitigation (SOBM) method utilizes dialogue segmentation to supplement order diversity, concurrently mitigating this challenge and order bias.Experiments demonstrate SADD’s effectiveness, achieving state-of-the-art results with a 6.52% F1 improvement.

## introduction dialogue aspect-based sentiment quadruple extraction task (diaasq) @xcite ) is a subtask of aspect-based sentiment analysis (absa), aiming to extract sentiment quadruples in dialogues, i.e., target: mentioned objects, aspect: components of targets, opinion: expressions conveying comments, and sentiment: polarity of targets. recently, @xcite proposed a discriminative model to control the information fusion among utterances, ultimately classifying different elements separately. however, this method fails to utilize the connections between tuple elements * corresponding author. ## task definition the input of the diaasq task is a n-utterance and n -word dialogue @xmath0, . . . , u n }, where u i represents the i-th utterance. diaasq aims to extract all quadruples (target, aspect, opinion, sentiment) from the dialogue, where the target, aspect, and opinion are sub-strings of d, and @xmath1, neg, other}. in the example "i didn't buy it since my friend said the xiaomi 11 has poor battery life," the corresponding quadruple is (xiaomi 11, battery life, poor, neg). ## method in the diaasq task, generation models face two significant challenges: noise and order bias. to mitigate noise, we propose a novel multi-granularity denoising generation approach involving sequence labeling, topic-aware dialogue segmenting, and denoising generation, as shown in fig. 2 . by employing sequence labeling and topicaware dialogue segmentation, we acquire denoising information at both the utterance and word levels. then, we integrate this multi-grained denoising information to guide the model in generating quadruples more accurately and robustly. for order bias, we uncover its cause as the gap between the actual and the ideal training objective. we propose a novel segmentation-aided order bias mitigation (sobm) method to narrow the gap with dialog segmentation. this method simultaneously addresses both the one-to-many training challenge and the order bias. ## conclusion this paper introduces a novel segmentation-aided multi-grained denoising and debiasing (sadd) model for denoising and debiasing in the diaasq task. for noise, we propose a multi-granularity denoising generation(mgdg) model to denoise at both word and utterance levels with denoised attention. for order bias, we analyze its direct causes and propose a distribution-based solution. we then introduce the segmentation-aided order bias mitigation (sobm) method, which utilizes dialogue segmentation to increase order diversity, thereby simultaneously alleviating the challenges of one-to-many learning and order bias. extensive experiments show sadd's sota performance.