Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract the aspect terms along with the corresponding opinion terms and the expressed sentiments in the review, which is an important task in sentiment analysis. Previous research efforts generally address the ASTE task in an end-to-end fashion through the table-filling formalization, in which the triplets are represented by a two-dimensional (2D) table of word-pair relations. Under this formalization, a term-level relation is decomposed into multiple independent word-level relations, which leads to relation inconsistency and boundary insensitivity in the face of multi-word aspect terms and opinion terms. To overcome these issues, we propose Boundary-Driven Table-Filling (BDTF), which represents each triplet as a relation region in the 2D table and transforms the ASTE task into detection and classification of relation regions. We also notice that the quality of the table representation greatly affects the performance of BDTF. Therefore, we develop an effective relation representation learning approach to learn the table representation, which can fully exploit both word-to-word interactions and relation-to-relation interactions. Experiments on several public benchmarks show that the proposed approach achieves state-of-the-art performances.

## introduction as a fine-grained task, aspect-based sentiment analysis (absa) focuses on the attitudes expressed on specific aspect terms @xcite . opinion terms refer to words or phrases expressing subjective sentiments. intuitively, opinion terms are important clues when determining the sentiment polarity and could provide a more detailed sentiment description for the aspect terms. previous work has generally concentrated on extracting aspect terms and opinion terms and classifying the sentiment expressed on the aspect term without explicitly considering the relations between aspect terms and opinion terms @xcite @xcite . therefore, @xcite propose the aspect sentiment triplet extraction (aste) task, which is exemplified in figure 1 . in the aste task, a triplet consists of an aspect term, the corresponding opinion term, and the expressed sentiment. @xcite adopt the pipeline approach to address the aste task. they first decompose the aste task into several subtasks and then learn models separately for each subtask. a more advanced alternative is learning a joint model to exploit the interactions between different subtasks @xcite @xcite @xcite . among these works, @xcite and @xcite tackle the aste task through a tablefilling approach, where the triplets are represented by a two-dimensional (2d) table of word-pair relations. in this approach, aspect terms and opinion terms are extracted through the diagonal elements of the table, and sentiments are treated as relation tags that are represented by the non-diagonal elements of the table. this formalization enables joint learning of different subtasks in aste, achieving superior performance over the pipeline approach. however, the previous table formalization suffers from relation inconsistency and boundary insensitivity when dealing with multi-word aspect terms and opinion terms. it decomposes the relation between an aspect term and an opinion term into the relations between the corresponding aspect words and opinion words. in other words, a term-level relation is represented by several wordlevel relation tags. the relation tags in the table are assigned independently, which leads to potential inconsistencies in the predictions of the wordlevel relations. in addition, when there are minor boundary errors in the aspect term or opinion term, the voting result for the term-level relation may stay unchanged, encouraging the model to produce wrong predictions. @xcite try to solve this problem through a span-based method, but their method discards fine-grained word-level information, which is the advantage of the table-filling approach. table-filling (bdtf) approach for aste to overcome the above issues. instead of decomposing aste into term extraction and relation classification, it extracts triplets by directly detecting and classifying the relation regions in a 2d table. specifically, we first detect all possible relation regions in the table through a region detection layer, which is enabled by predefined boundary tags. then we employ a region classifier to determine the sentiment label for each relation region. classification over the entire relation region ensures relation consistency, and those relation regions with boundary errors can be removed by being classified as invalid. to support the proposed bdtf, we also develop an effective relation representation learning approach to learn the table representation. we first learn the word-level contextualized representations of the input review through a pre-trained language model. then we adopt a tensor-based operation to construct the relation-level representations to fully exploit the word-to-word interactions. finally, we model relation-to-relation interactions through a multi-layer convolution-based encoder to enhance the relation-level representations. the relation representations of each two words in the review together form a 2d relation matrix, which serves as the table representation for bdtf. our contributions can be summarized as follows: table-filling (bdtf) approach for the aste task. it extracts aspect sentiment triplets from reviews by directly detecting and classifying the relation regions, overcoming relation inconsistency and boundary insensitivity of the previous methods.