PASTE : A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end. Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment. Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence. We report our results both with and without BERT and also demonstrate the utility of domain-specific BERT post-training for the task.

## introduction aspect-based sentiment analysis (absa) is a broad umbrella of several fine-grained sentiment analysis tasks, and has been extensively studied since its humble beginning in semeval 2014 @xcite . overall, the task revolves around * equal contribution sent 1: the film was good , but could have been better . triplets [aspect ; opinion ; sentiment] (1) film ; good ; positive (2) film ; could have been better ; negative sent 2: the weather was gloomy , but the food was tasty . automatically extracting the opinion targets or aspects being discussed in review sentences, along with the sentiments expressed towards them. early efforts on aspect-level sentiment classification @xcite @xcite focus on predicting the sentiment polarities for given aspects. however, in a real-world scenario, aspects may not be known a-priori. works on end-to-end absa @xcite @xcite thus focus on extracting the aspects as well as the corresponding sentiments simultaneously. these methods do not however capture the reasons behind the expressed sentiments, which could otherwise provide valuable clues for more effective extraction of aspect-sentiment pairs. consider the two examples shown in different from all these tagging-based methods, we propose to investigate the utility of a taggingfree scheme for the task. our innovation lies in formulating aste as a structured prediction problem. taking motivation from similar sequenceto-sequence approaches proposed for joint entityrelation extraction (nayak and ng, 2020; chen et al., 2021), semantic role labeling (fei et al., 2021) etc., we propose paste, a pointer networkbased encoder-decoder architecture for the task of aste. the pointer network effectively captures the aspect-opinion interdependence while detecting their respective spans. the decoder then learns to model the span-level interactions while predicting the connecting sentiment. an entire opinion triplet is thus decoded at every time step, thereby making our solution end-to-end. we note here however, that the aspect and opinion spans can be of varying lengths, which makes the triplet decoding process challenging. for ensuring uniformity, we also propose a position-based representation scheme to be suitably exploited by our proposed architecture. here, each opinion triplet is represented as a 5-point tuple, consisting of the start and end positions of the aspect and opinion spans, and the sentiment (pos/neg/neu) expressed towards the aspect. to summarize our contributions: ## our approach given the task of aste, our objective is to jointly extract the three elements of an opinion triplet, i.e., the aspect span, its associated sentiment, and the through exhaustive experiments, we investigate the utility of our approach and present a performance comparison with strong state-of-the-art baselines. ## related works absa is a collection of several fine-grained sentiment analysis tasks, such as aspect extraction @xcite , aspect-level sentiment classification @xcite , aspect-oriented opinion extraction @xcite , e2e-absa @xcite , and aspect-opinion co-extraction @xcite . however, none of these works offer a complete picture of the aspects being discussed. towards this end, @xcite recently coined the task of aspect sentiment triplet extraction (aste), and proposed a 2-stage pipeline solution. more recent end-to-end approaches such as ote-mtl @xcite , and gts @xcite fail to guarantee sentiment consistency over multi-word aspect/opinion spans, since they depend on word-pair dependencies. jet @xcite on the other hand requires two different models to be trained to detect aspect-overlapped and opinion-overlapped triplets. different from all these tagging-based methods, we propose a tagging-free solution for the aste task. ## conclusion we investigate the utility of a tagging-free scheme for the task of aspect sentiment triplet extraction using a pointer network-based decoding framework. addressing the limitations of previous tagging-based methods, our proposed architecture, paste, not only exploits the aspect-opinion interdependence during the span detection process, but also models the span-level interactions for sentiment prediction, thereby truly capturing the interrelatedness between all three elements of an opinion triplet. we demonstrate the better efficacy of paste, especially in recall, and in predicting multiple and/or overlapping triplets, when experimenting on the aste-data-v2 dataset.