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.

本文提出了一种基于 span-level 的双向网络框架，以更全面地从评论语句中提取三元组。但随着所有可能的跨度数量的显著增加，从中有效地提取三元组成分至关重要和具有挑战性，并且采纳 KL 散度扩展的类似跨度分离代价来促进区分正确跨度的下游任务，并采用推理策略根据其置信度分数从结果中删除冲突的三元组。