Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction (2022)

Abstract: 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 becausethey focus on the single interactions between an aspect and an opinion. To solve the aboveissues, we propose a novel multi-overlap triplet extraction method, which decodes the complexrelations between multiple aspects and opinions by learning their cooperative interactions. Overall, the method is based on an encoder-decoder architecture. During decoding, we design ajoint decoding mechanism, which employs a multi-channel strategy to generate aspects andopinions through the cooperative interactions between them jointly. Furthermore, we constructa correlation-enhanced network to reinforce the interactions between related aspectsand opinions for sentiment prediction. Besides, a relation-wise calibration scheme is adoptedto further improve performance. Experiments show that our method outperforms baselines,especially multi-overlap triplets.

## introduction aspect-based sentiment analysis (absa) @xcite is very important for natural language processing (atkinson-abutridy, 2022), which involves several fine-grained information extraction tasks @xcite @xcite @xcite @xcite . recently, aspect sentiment triplet extraction (aste) as an essential absa task has received widespread attention, which aims to extract triplets consisting of an aspect, an opinion, and their corresponding sentiment. generally, there are some overlapped triplets, especially multi-overlap triplets, due to ## related work the aste task includes three fundamental tasks: aspect term extraction @xcite , opinion term extraction @xcite , and aspectoriented sentiment classification @xcite . these fundamental tasks are point keys in solving aste task. as a compound task, aste has two main research lines, including tag-aware methods @xcite @xcite and span-aware methods @xcite @xcite . then, we introduce related works on each research line. tag-aware methods assign a single tag to each word by tagging schemes. @xcite utilize two bieos-based sequence tagging schemes to extract aspect-sentiment pairs and opinions and then identify their relations. @xcite leverage a unified sequence tagging scheme to jointly extract three factors of a triplet. @xcite propose a grid tagging scheme to tag relations between word-word pairs to fill a sentiment relation table. however, these methods limit the interactions between aspects and opinions by assigning a fixed tag to each word, and they ignore the impact of the relations between multiple aspects and opinions. therefore, they fail to solve multi-overlap triplets. span-aware methods identify the aspect and opinion spans by considering the start and end boundaries. @xcite propose a questiondriven method based on a reading comprehension scheme. they select one or more answers to a question to extract three factors of a triplet. then, @xcite propose a bidirectional questiondriven method to solve aste task. however, these methods focus on the interactions between a question and its answers. therefore, they still cannot fully solve multi-overlap triplets. @xcite propose a generation method based on a recurrent neural network (rnn) to decode entire triplets. however, they also cannot fully solve multi-overlap triplets due to the limitation of the single interactions between an aspect and an opinion. @xcite propose a unified generation framework to extract triplets through a sequence output. however, they still suffer from multi-overlap triplets. unlike the above methods, our method considers the complex relations between aspects and opinions and captures their cooperative interactions to solve multi-overlap triplets. ## conclusion we propose a multi-overlap triplet extraction method to explore the complex relations between multiple aspects and opinions by learning their cooperative interactions. it addresses the limitation that most methods focus on the single interactions between an aspect and an opinion while decoding three factors of a triplet. the ate and ote tasks are solved in the decoding process through a joint decoding mechanism. and a correlationenhanced network reinforces the interactions between related aspects and opinions while predicting their sentiments. our method obtains convincing improvements on overlapped triplets, especially multi-overlap triplets.