Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024)

Abstract: In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promisingresults. However, they still suffer from imprecise predictions and limited interpretability, caused by data scarcity and inadequate modeling of the quadruplet composition process. In this paper, we propose Self-Consistent Reasoning-based Aspect sentiment quadruple Prediction (SCRAP), optimizing its model to generate reasonings and the corresponding sentiment quadruplets in sequence. SCRAP adopts the Extract-Then-Assign reasoning strategy, which closely mimics human cognition. In the end, SCRAP significantly improves the model’s ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in ASQP.

## introduction aspect-based sentiment analysis (absa) refers to the task of identifying entity aspects and their associated sentiments @xcite . among various absa tasks, the challenging task of predicting quadruplets, including aspect sentiment quad prediction (asqp) @xcite and aspect-category-opinion-sentiment (acos) @xcite , has garnered significant interest in that it can offer comprehensive aspect-level analysis. specifically, a quadruplet consists of four sentiment elements: aspect term (at), opinion term (ot), aspect category (ac), and sentiment polarity (sp). recent studies have developed powerful generative methods by fine-tuning language models (lms) to sequentially generate sentiment quads @xcite @xcite ). * equal contribution † corresponding author 1 codes and datasets are available at https://github. com/jieyong99/scrap "this place has ruined me for neighborhood sushi." without reasoning with "extract-then-assign" reasoning 🤔 human ✅ ❌ [at]place [ot]ruined [ac]restaurant [sp]negative [at]sushi [ot]ruined [ac]food [sp]positive lm let me see... here, the aspect is "sushi" and its opinion is "ruined". based on "sushi" and "ruined", the category is "food" and it is a "positive" sentence! although state-of-the-art generative methods for asqp achieve promising accuracy, they are hindered by imprecise predictions and limited interpretability, stemming from data scarcity and inadequate modeling of the quadruplet composition process. in figure 1 left, the existing approach that directly decodes quads from a sentence, not only fails to make accurate predictions, but also struggles to discern the reasoning behind the quadruplet. to tackle this challenge in asqp, our approach introduces a two-step reasoning strategy, termed extract-then-assign. this reasoning strategy initially extracts all aspect terms and opinion terms from an input sentence; subsequently, it assigns each aspect-opinion pair to both an aspect category and a sentiment polarity, utilizing predefined sets of categories and polarities. in figure 1 right, the process begins with a human extracting the information (at, ot) that is directly identifiable from the review sentence. following this, the individual infers ac and sp based on the initially extracted components. we explicitly model these two steps of asqp reasoning, which can account for the generation of complete quadruplets. this reasoning process, which closely mimics human cognition, allows us to enhance the accuracy and interpretability scrap gross food (…) [at]food [ot]gross [ac]food quality [sp]negative gross food (…) prompt sentence + quad diverse reasoning + permuted quad sentence [at]food [sp]negative [ot]gross [sp]negative [at]food [ot]gross [ac]food quality [at]food [ot]gross [ac]food quality [sp]negative [ac]food quality [sp]negative [at]food [ot]gross [ac]food quality [sp]negative in this sentence, … therefore, the quadruplets are step 3. self-consistent quad prediction with diverse reasoning drinks way over priced (…) [sp]negative [at]drinks [ot]over [ac]drinks quality in this sentence, … [at]drinks [ot]priced [ac]drinks prices [sp]positive in this sentence, … [at]drinks [ot]over priced [ac]drinks prices [sp]negative in this sentence, … [at]drinks [ot]over priced [ac]drinks prices [sp]negative in this sentence, … scrap … sentence ✅ ✅ ❌ [at]drinks [ot]over priced [ac]drinks prices [sp]negative of quad prediction. in this work, we propose a novel framework for self-consistent reasoning-based aspectsentiment quad prediction, named scrap, which predicts aspect sentiment quads based on the extract-then-assign reasoning (figure 2 ). the key idea is to distill the plausible reasoning ability from large language models (llms) into our asqp model. to this end, we first collect diverse reasoning paths via chain-of-thought (cot) prompting on llms @xcite , and then optimize our model to generate the reasoning followed by quad prediction. furthermore, scrap aggregates its diverse reasoning outputs based on the self-consistency @xcite . this allows for filtering out noisy outputs and achieving more accurate quad predictions. extensive experiments on two asqp benchmarks demonstrate that scrap significantly outperforms other state-of-the-art quad prediction models. our reasoning process provides the explanation about the results and improves the prediction accuracy by understanding the inherent structure and relationships within the aspect-opinion pairs. ## experiments we experiment to answer the following questions: rq1: does scrap outperform other baselines? rq2: how do diverse reasoning paths in scrap contribute to achieving higher accuracy? rq3: does extract-then-assign reasoning help to interpret quad prediction? ## related work aspect sentiment quad prediction many existing studies have focused on discriminative methods. early works tried jointly detecting target-aspectsentiment @xcite or conducting acos with two-stage pipelines @xcite . beginning with @xcite , recent studies have started using generative methods. zhang et al. (2021a) transforms outputs into natural language sentences, hu et al. (2022) introduce data augmen-tation that uses various permutations of quad elements, and gou et al. (2023) align training and inference with multi-view prompting. ## conclusion this paper aims to enhance quad prediction in asqp using extract-then-assign, which is the twostep reasoning strategy. to this end, we propose a scrap framework, which generates diverse predictions utilizing the extract-then-assign and selects the final answer by filtering the inconsistent answers through consistency voting. our framework is the first method to integrate reasoning into the absa task, not only achieving state-of-theart performance, but also significantly enhancing the interpretability of the outputs produced by the model. this confirms its efficacy in predicting quadruplets through reasoning.