A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. Recently, span-level models achieve gratifying results on ASTE task by taking advantage of the predictions of all possible spans. Since all possible spans significantly increases the number of potential aspect and opinion candidates, it is crucial and challenging to efficiently extract the triplet elements among them. In this paper, we present a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. Specifically, we devise both the aspect decoder and opinion decoder to decode the span representations and extract triples from aspect-to-opinion and opinion-to-aspect directions. With these two decoders complementing with each other, the whole network can extract triplets from spans more comprehensively. Moreover, considering that mutual exclusion cannot be guaranteed between the spans, we design a similar span separation loss to facilitate the downstream task of distinguishing the correct span by expanding the KL divergence of similar spans during the training process; in the inference process, we adopt an inference strategy to remove conflicting triplets from the results base on their confidence scores. Experimental results show that our framework not only significantly outperforms state-of-the-art methods, but achieves better performance in predicting triplets with multi-token entities and extracting triplets in sentences contain multi-triplets.

## introduction aspect-based sentiment analysis (absa) is an important field in natural language processing (nlp). the absa task contains various fundamental subtasks, such as aspect term extraction (ate), opinion term extraction (ote), and aspect-level sentiment classification (asc). recent studies focus on solving these tasks individually or doing a combination of two subtasks, such as aspect term polarity co-extraction (apce), aspect opinion coextraction (aoce), and aspect-opinion pair extraction (aope). however, none of these subtasks aims to extract the aspect terms (at) with their corresponding opinion terms (ot) and sentiment polarity (sp) simultaneously. to tackle this problem, @xcite propose the aspect sentiment triplet extraction (aste) task which aims to extract (at, ot, sp) triplets such as (hot dogs, top notch, positive) and (coffee, average, negative) in the example of figure 1 . the span-to-span interactions among ats and ots by enumerating all possible spans as input. despite the exciting results their work has yielded, several challenges remain with the existing span-level model. first, since both aspect terms and opinion terms can trigger triplets, it is a challenge to identify triplets bidirectionally. second, unlike tokenlevel methods, span-level input cannot guarantee mutual exclusivity among the spans, so the similar spans (spans that have shared tokens) such as hot dogs, dogs, and the hot dogs, may cause confusion in downstream tasks. thus, it is challenging for span-level models to effectively distinguish these similar span. third, the existence of similar spans enables span-level models to generate conflicting triples in the results, such as (hot dogs, top notch, positive), (hot dogs,are top notch, positive), and (the hot dogs, top notch, positive). how to properly extract non-conflicting triplets is also challenging. to address these challenges, we propose a spanlevel bidirectional network for aste task. unlike prior span-level works @xcite , our network decodes all possible span representations from both aspect-to-opinion and opinion-to-aspect directions through the cooperation of the aspect decoder and opinion decoder. in the aspect-to-opinion direction, the aspect decoder aims to extract ats such as {hot dogs, coffee}, and the opinion decoder aims to extract ots such as {top notch} for each specific at like {hot dogs}. analogously, in the opinion-to-aspect direction, the opinion decoder and aspect decoder are utilized to extract ots and their corresponding ats, respectively. furthermore, we design the similar span separation loss to direct the model deliberately distinguishing similar span representations during the training process; and an inference strategy employed in the prediction process is also proposed for eliminating the conflicting triplets in the extraction results. to verify the effectiveness of our framework, we conduct a series of experiments based on four benchmark datasets. the experimental results show our framework substantially outperforms the existing methods. in summary, our contributions are as follows: ## related work aspect based sentiment analysis (absa) is a finegrained sentiment analysis task that consists of various subtasks, including aspect term extraction (ate) @xcite @xcite @xcite , opinion term extraction (ote) @xcite @xcite , aspect-level sentiment classification (asc) @xcite @xcite . since these subtasks are solved individually, recent studies attempted to couple two subtasks as a compound task, such as aspect term polarity co-extraction (apce) @xcite @xcite , aspect and opinion co-extraction @xcite @xcite , aspect category and sentiment classification @xcite , and aspect-opinion pair extraction (aope) @xcite @xcite , and aspect-opinion pair extraction (aope) @xcite @xcite . although many works have achieved great progress on these tasks, none of these tasks aims to identify the aspect terms as well as their corresponding opinion term and sentiment polarity. to tackle this issue, @xcite proposed the aspect sentiment triplet extraction (aste) task, which aimed to extract aspect terms, the sentiments of the aspect terms, and the opinion terms causing the sentiments. some methods @xcite designed a unified tagging scheme to solve this task. some others @xcite formulated this task as a multi-turn machine reading comprehension task and solve it with machine reading comprehension frameworks. recently, @xcite had propose a span-level model to extract ats and ots first and then predict the sentiment relation for each (at, ot) pairs. ## methodology as shown in figure 2 , our network consists of four parts: span generation, similar span separation loss, bidirectional structure, and the inference strategy. in the following subsections, we first give the definition of aste tasks and then detail our network structure. ## conclusions in this work, we propose a span-level bidirectional network for aste tasks. this span-level model can take advantages from both aspect-to-opinion and opinion-to-aspect directions. the bidirectional decoding can ensure that either an at or an ot can trigger an aspect sentiment triplet, which is more in line with human perception. for the shortcoming that mutual exclusivity cannot be guaranteed among spans, we deploy the similar span separation loss to guide the model in discriminating similar spans. we further design an inference strategy to eliminate conflicting triplet results that are specific to span-level models. the experimental results demonstrate that our network significantly outperforms the compared baselines and achieves state-of-the-art performance.