Dual-Channel Span for Aspect Sentiment Triplet Extraction (2023)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine-grained aspect-based sentiment analysis (ABSA), aiming at extracting the triplets of aspect terms, corresponding opinion terms and the associated sentiment orientation. Recent efforts in exploiting span-level semantic interaction shown superior performance on ASTE task. However, most of the existing span-based approaches suffer from enumerating all possible spans, since it can introduce too much noise in sentiment triplet extraction. To ease this burden, we propose a dual-channel span generation method to coherently constrain the search space of span candidates. Specifically, we leverage the syntactic relations among aspect/opinion terms and the associated part-of-speech characteristics in those terms to generate span candidates, which reduces span enumeration by nearly half. Besides, feature representations are learned from syntactic and part-of-speech correlation among terms, which renders span representation fruitful linguistic information. Extensive experiments on two versions of public datasets demonstrate both the effectiveness of our design and the superiority on ASTE/ATE/OTE tasks.

## introduction aspect sentiment triplet extraction (aste) is a compound task in fine-grained aspect-based sentiment analysis (absa) @xcite . it is composed of three fundamental subtasks: aspect term extraction (ate) @xcite @xcite , opinion term extraction (ote) @xcite @xcite and aspect sentiment classification (asc) @xcite @xcite @xcite . in particular, aste aims to extract the sentiment triplet of aspect terms, corresponding opinion terms and their associated sentiment polarity in a given sentence. for example, in the sentence "my vegetable risotto was burnt, and infused totally in a burnt flavor", there are two sentiment triplets, namely, ("vegetable risotto", "burnt", negative") and ("flavor", "burnt", negative"), where "vegetable risotto" and "flavor" are aspect terms, "burnt" is the opinion term corresponding to the aspect of interest, and "negative" is the sentiment polarity of these two triplets. when the idea of aste was first proposed, a two-stage pipeline method @xcite was developed for this task. however, staged processing scheme often lead to error propagation between subtasks. more than that, opinion terms are generally associated with the aspect target, staged pipeline method breaks this interaction. to address those issue, some end-to-end approaches @xcite @xcite are devised, which attempt to simultaneously extract aspect-opinion pairs and perform sentiment classification by introducing novel tagging schemes. in particular, most of existing end-toend models @xcite @xcite build the interaction between aspect and its corresponding opinion at token-level, i.e., word-toword interactions. despite of its efficacy, it is hard to guarantee the consistency of predicted sentiment polarity between multiple word-to-word pairs when many aspects/opinions are expressed using multiple words. on account of this, recent work @xcite adopt span-level interactions in the sentiment triplet structure. compared with the token-level pairing, span-level in-teraction is proved to bring significant gains to the model. however, one prominent problem with spanbased methods is that they usually enumerate all spans in a sentence, which will bring about high computational cost and many noises. specifically, the number of enumerated spans for a sentence of length n is o(n 2 ), while the number of possible interactions between all opinion and aspect candidate spans is o(n 4 ) at the later span-pairing stage, implying a lot of invalid aspect/opinion spans and span pairs. moreover, most of the existing spanbased methods model direct interactions between two spans. the high-order interactions are generally overlooked. to address those issues, we explore the linguistic phenomena in the spans. our observations are two-fold: first, multiple words composed of the span of an aspect/opinion target are generally syntactically dependent, and multiple dependency relations can transmit higher-order interactions between spans. for example, in figure 1 , the aspect term "vegetable risotto" has an intra-span syntactic dependency "compound" and an inter-span dependency "nsubj" with "burnt". on the other hand, the span "flavor" is indirectly related to the first "burnt" (i.e., the one associated with "vegetable risotto") within 2 hops in the syntactic tree. this indirect relation may suggest the relevance of the sentiment polarity of the two span pairs, namely, ("vegetable risotto", "burnt") and ("flavor", "burnt"). in effect, the ground truth of sentiment polarity of these two aspect-opinion pairs are the same, as shown in figure 1 . second, we also observe that there are some frequent patterns in aspect and opinion spans in terms of part-of-speech. for instance, in many cases aspect terms are noun or noun phrase which we refer to as n n and (n n -n n ), respectively. moreover, it is fairly common that opinion terms are adjective (denoted by jj). as shown in figure 1 , the aspect "vegetable risotto" has the part-ofspeech structure (n n -n n ), and opinion term "burnt" is jj. therefore, it is possible to extract the aspect/opinion spans according to the lexical characteristics of the words so as to avoid enumerating all word combination. motivated by the two observations, we propose a dual-channel span generation approach for aspectlevel sentiment triplet extraction, which we term as dual-span. dual-span utilizes two relational graph attention networks (rgat) to separately learn high-order syntactic dependency between words/spans and linguistic features in constructed part-of-speech relations among words. then a gating mechanism is adopted to fuse the syntactic and lexical information of span candidates, which helps to enhance the feature representation of spans. on the other hand, instead of enumerating all possible spans, the span candidates are extracted from two channels, i.e., the syntactic dependency relations and part-of-speech based relations, thus largely reducing the noisy information in favor of valid span pairing. our main contributions are as follows: ## related work aspect-based sentiment analysis (absa) @xcite @xcite @xcite is fine-grained sentiment analysis. the early work of absa was to identify its three sentiment elements (i.e., aspect, opinion, sentiment polarity) as basic tasks: ate (e.g., @xcite @xcite , ote @xcite @xcite ) and asc (e.g., @xcite @xcite ; li et al., 2021; brauwers and frasincar, 2023)). subsequently, some studies began to consider multiple sentiment element composite tasks in order to better understand fine-grained sentiment analysis: aspect term polarity co-extraction (apce) (li and lu, 2017; he et al., 2019; li et al., 2019), aspect-opinion pair extraction (aope) (zhao et al., 2020 @xcite @xcite and aspect category sentiment analysis (acsa) @xcite @xcite . some recent works started to consider the integrity among the three sentiment elements and thus proposed the aste task. a diversity of techniques were proposed for it: two-stage pipeline @xcite , multi-task unified framework @xcite @xcite , multi-round machine reading comprehension method @xcite @xcite and end-to-end method @xcite @xcite @xcite . the span-level based approaches adopt end-to-end implementation. for instance, span-aste @xcite enumerates aspect and viewpoint spans and directly exploits their interaction to solve aste tasks, while sbn @xcite proposed a span-level bidirectional network that enumerates all possible spans as input, and completes the aste task by designing two decoders and adopting inference strategies. despite that, it still remains an open challenge to improve the search efficiency and feature representation for the span of sentiment triplets. ## proposed framework in this section, the overall architecture of our proposed model dual-span is shown in figure 2 , which consists of four main components: sentence encoding, feature enhancing module, dual-channel span generation and triplet module. ## conclusion in this work, we present a dual-span model for improving the performance on aste task. based on the observations of syntactic relations and partof-speech features among spans, we design a dualchannel span generation method to refine the span candidate set so as to mitigate the negative impacts of invalid spans. moreover, we employ relational graph neural networks to capture the high-order interactions between possible spans from both views: syntactic dependency relation and part-of-speech relation. our experimental results demonstrate that the proposed method brings meaningful gains to aste as well as ate task, compared to all baselines. we also note that for ote task, our method is generally inferior to the vanilla span-based method that enumerate all possible spans. the reason may lie in the limited part-of-speech relations, which will be considered in the future work.