Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction

Aspect sentiment triplet extraction (ASTE) is an essential task, which aims to extract triplets (aspect, opinion, sentiment).However, overlapped triplets, especially multi-overlap triplets, make ASTE a challenge.Most existing methods suffer from multi-overlap triplets because they focus on the single interactions between an aspect and an opinion.To solve the above issues, we propose a novel multi-overlap triplet extraction method, which decodes the complex relations between multiple aspects and opinions by learning their cooperative interactions.Overall, the method is based on an encoder-decoder architecture.During decoding, we design a joint decoding mechanism, which employs a multi-channel strategy to generate aspects and opinions through the cooperative interactions between them jointly.Furthermore, we construct a correlation-enhanced network to reinforce the interactions between related aspects and opinions for sentiment prediction.Besides, a relation-wise calibration scheme is adopted to further improve performance.Experiments show that our method outperforms baselines, especially multi-overlap triplets.