COM - MRC : A CO ntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. In this paper, we propose a novel COntext-Masked MRC (COM-MRC) framework for ASTE. Our COM-MRC framework comprises three closely-related components: a context augmentation strategy, a discriminative model, and an inference method. Specifically, a context augmentation strategy is designed by enumerating all masked contexts for each aspect term. The discriminative model comprises four modules, i.e., aspect and opinion extraction modules, sentiment classification and aspect detection modules. In addition, a two-stage inference method first extracts all aspects and then identifies their opinions and sentiment through iteratively masking the aspects. Extensive experimental results on benchmark datasets show the effectiveness of our proposed COM-MRC framework, which outperforms state-of-the-art methods consistently.

## introduction aspect sentiment triplet extraction (aste) has recently been proposed, which is a variant of the finegrained aspect-based sentiment analysis (absa) task. for a given sentence, aste aims to extract the sentiment triplets, including aspect term, opinion term and the corresponding sentiment polarity. as shown in figure 1 , aste could produce two triplets from the given sentence. for aste task, early methods adopt a two-stage pipeline framework that first identifies aspects with sentiment and opinions then pairs them, producing nice ambience , but highly overrated place . the sentiment triplets @xcite . however, these pipeline-based methods ignore the interaction among triplets, which could result in the error propagation. to alleviate this problem, some recent studies jointly extract the sentiment triplets in an end-to-end framework @xcite @xcite @xcite , which is constructed mainly by designing a tagging scheme. very recently, @xcite and @xcite formalized aste by using a machine reading comprehension (mrc) framework. the basic idea of mrc-based methods is using multiturn qa under an identical context with diverse queries. specifically, mrc-based methods involve two stages, aspect inference (ai) and aspect accessory inference (aai). the former is to extract aspect terms by constructing a query about aspects, e.g., "what aspects?". the latter is to identify the corresponding opinions and sentiment by constructing queries for each aspect term, e.g., "what opinions and sentiment given the aspect ambience?". despite the impressive performance, however, mrc-based methods may suffer the interference problem when analyzing sentences with multiple aspects. intuitively, the more aspects a sentence in this paper, we propose a novel framework called context-masked machine reading comprehension (com-mrc) for aste. our com-mrc framework is in general based on the idea of masking aspect, and it comprises three closely-related components: a context augmentation strategy, a discriminative model, an inference method. firstly, to alleviate the interference and to better identify information from different aspects, we use the idea of masking aspects for context augmentation. we argue that a sentence with multiple aspect terms deserves to be treated as multiple training samples due to the difficulty of extracting triplets. hence, we set a regular query with various masked contexts to identify each aspect and its accessories. for a sentence with t aspect terms, the number of samples grows from 1 to 2 t . thus, the training corpus effectively expands. secondly, to effectively capture the correlation among sentiment triplets, we design a discriminative model. an aspect de-tection module detects whether there exist aspect terms in the masked context. this module and the other three modules, i.e., aspect extraction, opinion extraction, and sentiment classification modules, work collaboratively for aste task. thirdly, the aspect is extracted one by one from left to right during ai stage through iteratively masking aspects. then, during aai stage, all other unrelated aspects are masked in the context for more precise identification. the three components constitute our com-mrc framework which could alleviate the interference problem. experimental results show that our com-mrc framework consistently outperforms state-of-the-art methods. our contributions are summarized as follows. ## conclusion and future work in this paper, we propose a novel context-masked mrc (com-mrc) framework to alleviate the interference problem in the aste task. our com-mrc comprises three close-related components. the context augmentation can effectively expand the training corpus. the discriminative model comprising four modules works collaboratively. our inference method involving two stages can effectively reduce the interference from other aspects. extensive experiments on two groups of benchmark datasets demonstrate the effectiveness of our com-mrc framework. in the future, we will devote to devising a one-stage method with a faster inference.