A Multi-task Learning Framework for Opinion Triplet Extraction (2020)

Abstract: The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.

## introduction aspect-based sentiment analysis (absa), also termed as target-based sentiment analysis in some literature @xcite , is a fine-grained sentiment analysis task. it is usually formulated as detecting aspect terms and sentiments expressed in a sentence towards the aspects @xcite @xcite . this type of formulation is referred to as aspect-sentiment pair extraction. meanwhile, there exists another type of approach to absa, referred to as aspectopinion co-extraction, which focuses on jointly deriving aspect terms (a.k.a. opinion targets) and example sentence: the atmosphere is attractive , but a little uncomfortable . aspect-sentiment pair extraction : [(atmosphere, positive), (atmosphere, negative)] aspect-opinion co-extraction : @xcite opinion triplet extraction : [(atmosphere, attractive, positive), (atmosphere, uncomfortable, negative)] opinion terms (a.k.a. opinion expressions) from sentences, yet without figuring out their sentiment dependencies @xcite . the compelling performances of both directions illustrate a strong dependency between aspect terms, opinion terms and the expressed sentiments. this motivates us to put forward a new perspective for absa as joint extraction of aspect terms, opinion terms and sentiment polarities, foot_0 in short opinion triplet extraction. an illustrative example of differences among aspect-sentiment pair extraction, aspect-opinion co-extraction, and opinion triplet extraction is given in figure 1 . opinion triplet extraction can be viewed as an integration of aspect-sentiment pair extraction and aspect-opinion co-extraction, by taking into consideration their complementary nature. it brings in two-fold advantages: (1) the opinions can boost the expressive power of models and help better determine aspectoriented sentiments; (2) the sentiment dependencies between aspects and opinions can bridge the gap of how sentiment decisions are made and further promote interpretability of models. there is some prior research with a similar viewpoint. @xcite proposes to extract opin-ion tuples, i.e., (aspect-sentiment pair, opinion)s, by first jointly extracting aspect-sentiment pairs and opinions by two sequence taggers, in which sentiments are attached to aspects via unified tags, 3 and then pairing the extracted aspect-sentiments and opinions by an additional classifier. despite of remarkable performance the approach has achieved, two issues need to be addressed. the first issue arises from the prediction of aspects and sentiments with a set of unified tags thus degrading the sentiment dependency parsing process to a binary classification. as is discussed in prior studies on aspect-sentiment pair extraction @xcite @xcite , although the concerned framework with unified tagging scheme is theoretically elegant and mitigates the computational cost, it is insufficient to model the interaction between the aspects and sentiments @xcite . secondly, the coupled aspect-sentiment formalization disregards the importance of their interaction with opinions. such interaction has been shown important to handle the overlapping circumstances where different triplet patterns share certain elements, in other triplet extraction-based tasks such as relation extraction @xcite . to show why triplet interaction modelling is crucial, we divide triplets into three categories, i.e., aspect overlapped, opinion overlapped, and normal ones. examples of these three kinds of triplets are shown in figure 2 . we can observe that two triplets tend to have the same sentiment if they share the same aspect or opinion. hence, modelling triplet interaction shall benefit the asba task, yet it can not be explored with the unified aspect-sentiment tags in which sentiments have been attached to aspects without considering the overlapping cases. to circumvent the above issues, we propose a multi-task learning framework for opinion triplet extraction, namely ote-mtl, to jointly detect aspects, opinions, and sentiment dependencies. on one hand, the aspects and opinions can be extracted with two independent heads in the multi-head architecture we propose. on the other hand, we decouple sentiment prediction from aspect extraction. instead, we employ a sentiment dependency parser as the third head, to predict word-level sentiment dependencies, which will be utilized to further decode span-level foot_1 dependencies when incorporated with the detected aspects and opinions. in doing so, we expect to alleviate issues brought by the unified tagging scheme. specifically, we exploit sequence tagging strategies @xcite for extraction of aspects and opinions, whilst taking advantage of a biaffine scorer @xcite to obtain word-level sentiment dependencies. additionally, since these task-heads are jointly trained, the learning objectives of aspect and opinion extraction could be considered as regularization applied on the sentiment dependency parser. in this way, the parser is learned with aspect-and opinion-aware constraints, therefore fulfilling the demand of triplet interaction modelling. intuitively, if we are provided with a sentence containing two aspects but only one opinion (e.g., the third example in figure 2 ), we can identify triplets with overlapped opinion thereby. extensive experiments are carried out on four semeval benckmarking data collections for absa. our framework are compared with a range of stateof-the-art approaches. the results demonstrate the effectiveness of our overall framework and individual components within it. a further case study shows that how our model better handles overlapping cases. ## conclusions and future work our work put forwards an opinion triplet extraction perspective for aspect-based sentiment analysis. existing works that are applicable to opinion triplet extraction have been shown insufficient, owing to the use of unified aspect-sentiment tagging scheme and ignorance of the interaction between elements in the triplet. thus, we propose a multi-task learning framework to address the limitations by highlighting the uses of joint training, decoupled aspect and sentiment prediction, and regularization among correlated tasks during learning. experimental results verify the effectiveness of our framework in comparison with a wide range of strong baselines. comparison results with different variants of the proposed framework signify the necessity of the core components in the framework. based on the observations from a case study and error analysis, we plan to carry out further research in the following aspects: (1) more robust taggers for aspect and opinion extraction, (2) more flexible evaluation metric for triplet extraction, and (3) more mighty triplet interaction mechanism (e.g., encoder decoder structure).