Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction (2021)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions.

## introduction aspect-based sentiment analysis (absa) @xcite is an aggregation of several fine-grained sentiment analysis tasks, and its various subtasks are designed with the aspect target as the fundamental item. for the example in figure 1 : an example of aste. the spans highlighted in orange are target terms, and the span in blue is opinion term. the "-" on top of target terms indicates negative sentiment. figure 1 , the aspect targets are "windows 8" and "touchscreen functions". aspect sentiment classification (asc) @xcite @xcite @xcite is one of the most well-explored subtasks of absa and aims to predict the sentiment polarity of a given aspect target. however, it is not always practical to assume that the aspect target is provided. aspect term extraction (ate) @xcite @xcite focuses on extracting aspect targets, while opinion term extraction (ote) @xcite @xcite aims to extract the opinion terms which largely determine the sentiment polarity of the sentence or the corresponding target term. aspect sentiment triplet extraction (aste) @xcite is the most recently proposed subtask of absa, which forms a more complete picture of the sentiment information through the triplet of an aspect target term, the corresponding opinion term, and the expressed sentiment. for the example in figure 1 , there are two triplets: ("windows 8", "not enjoy", negative) and ("touchscreen functions", "not enjoy", negative). the initial approach to aste @xcite ) was a two-stage pipeline. the first stage extracts target terms and their sentiments via a joint labeling scheme 2 , as well as the opinion terms with stan-dard bioes 3 tags. the second stage then couples the extracted target and opinion terms to determine their paired sentiment relation. we know that in absa, the aspect sentiment is mostly determined by the opinion terms expressed on the aspect target @xcite . however, this pipeline approach breaks the interaction within the triplet structure. moreover, pipeline approaches usually suffer from the error propagation problem. recent end-to-end approaches @xcite @xcite can jointly extract the target and opinion terms and classify their sentiment relation. one drawback is that they heavily rely on word-to-word interactions to predict the sentiment relation for the target-opinion pair. note that it is common for the aspect targets and opinions to contain multiple words, which accounts for roughly one-third of triplets in the benchmark datasets. however, the previous methods @xcite predict the sentiment polarity for each word-word pair independently, which cannot guarantee their sentiment consistency when forming a triplet. as a result, this prediction limitation on triplets that contain multi-word targets or opinions inevitably hurts the overall aste performance. for the example in figure 1 , by only considering the word-to-word interactions, it is easy to wrongly predict that "enjoy" expresses a positive sentiment on "windows". @xcite proposed a position-aware tagging scheme to allow the model to couple each word in a target span with all possible opinion spans, i.e., aspect word to opinion span interactions (or vice versa, aspect span to opinion word interactions). however, it still cannot directly model the span-tospan interactions between the whole target spans and opinion spans. in this paper, we propose a span-based model for aste (span-aste), which for the first time directly captures the span-to-span interactions when predicting the sentiment relation of an aspect target and opinion pair. of course, it can also consider the single-word aspects or opinions properly. our model explicitly generates span representations for all possible target and opinion spans, and their paired sentiment relation is independently predicted for all possible target and opinion pairs. span-based methods have shown encouraging perof a target span with positive sentiment polarity. our contribution can be summarized as follows: ## related work sentiment analysis is a major natural language understanding (nlu) task @xcite and has been extensively studied as a classification problem at the sentence level @xcite @xcite . aspect-based sentiment analysis (absa) @xcite addresses various sentiment analysis tasks at a finegrained level. as mentioned in the section 1, the subtasks mainly include asc @xcite @xcite @xcite @xcite @xcite , ate @xcite @xcite , ote (hu @xcite @xcite . there is also another subtask named target-oriented opinion words extraction (towe) @xcite , which aim to extract the corresponding opinion words for a given target term. another line of research focuses on addressing different subtasks together. aspect and opinion term co-extraction (aote) aiming to extract the aspect and opinion terms together @xcite @xcite and is often treated as a sequence labeling problem. note that aote does not consider the paired sentiment relationship between each target and opinion term. end-to-end absa @xcite @xcite jointly extracts each aspect term and its associated sentiment in an end-to-end manner. a few other methods are recently proposed to jointly solve three or more subtasks of absa. chen and qian (2020) proposed a relation aware collaborative learning framework to unify the three fundamental subtasks and achieved strong performance on each subtask and combined task. while @xcite focused more on aspect category related subtasks, such as aspect category extraction and aspect category and target joint extraction. aste @xcite @xcite is the most recent development of absa and its aim is to extract and form the aspect term, its associated sentiment, and the corresponding opinion term into a triplet. ## conclusions in this work, we propose a span-level approach -span-aste to learn the interactions between target spans and opinion spans for the aste task. it can address the limitation of the existing approaches that only consider word-to-word interactions. we also propose to include the ate and ote tasks as supervision for our dual-channel pruning strategy to reduce the number of enumerated target and opinion candidates to increase the computational efficiency and maximize the chances of pairing valid target and opinion candidates together. our method significantly outperforms the previous methods for aste as well as ate and ote tasks and our analysis demonstrates the effectiveness of our approach. while we achieve strong performance on the aste task, the performance can be mostly attributed to the improvement on the multi-word triplets. as discussed in section 4.1, there is still a significant performance gap between single-word and multiword triplets, and this can be a potential area for future work.