Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022)

Abstract: Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance.

## introduction compared with traditional sentence-level or document-level sentiment analysis tasks, aspectbased sentiment analysis (absa) requires finer grained analysis on the texts and extracting more detailed information @xcite . absa contains many subtasks, such as aspect category detection, opinion term extraction (oe), etc. aspect term extraction (ae) and aspectlevel sentiment classification (sc) are two elemental subtasks of absa. ae means extracting the sentiment aspects from a given plain sentence and sc implies recognizing the sentiment polarities of the given aspects in a sentence. combining the above two subtasks, aspect term extraction and sentiment classification (aesc) establishes the third fundamental subtask. the ae, sc and aesc for the sentence excellent food, although the interior could use some help. are given in figure 1 . in general, the existing mainstream approaches can be roughly divided into two brands. the first ae needs to identify the aspect terms food and interior. when given these two terms, sc should recognize the sentiment polarities of them as positive and negative separately. aesc is going to complete these two tasks from the given sentence. in recent years, machine reading comprehension @xcite @xcite quickly become a hot topic among various challenging natural language understanding tasks. generally, mrc model may give a proper answer for a query based on a given passage. there are various types of mrc tasks according to the desired answer forms, among which span mrc or extractive mrc draw quite a lot of attention @xcite @xcite . most of the progress for mrc may be attributed to the latest pre-trained language models (prlms) the waiters were very professional, courteous and attentive. ## related work open-domain sentiment analysis or absa requires to extracting the aspect terms with their corresponding sentiment polarities in the open domain, which is an active research topic in recent years. early studies treat them as two separate tasks and use some traditional algorithms such as conditional random fields (crf) to complete the task. @xcite apply recursive neural crf to perform absa. @xcite use a lifelong learning crf to extract the prior knowledge of past domains. with the rise of deep learning technology, more and more models based on neural networks have begun to emerge. @xcite @xcite take convolutional neural network (cnn) to handle absa tasks. in addition, some researchers apply rnn and also make some progress @xcite @xcite . in recent years, more studies have proposed diverse attention mechanisms to extract more knowledge from the text and boost the performance of the model. @xcite put forward a novel layer containing two attentions to extract sentiment aspects, opinions and polarities. li et al. (2018) recently, offering promising performance, prlm has become an important and rapid devel-opment area in the field of natural language processing. however, minority studies directly utilize the knowledge of prlm. even if the current stateof-art model @xcite , it merely regards bart @xcite as a powerful seq-to-seq model without using its erudition. for extracting the knowledge from prlm, mrc is an excellent solution. @xcite convert named entity recognition into an mrc task and achieve the stateof-the-art. @xcite design a dual-mrc framework for absa and get the promising result. @xcite employ mrc to handle dependency parsing. yu et al. (2021) and chen et al. (2021a ) respectively propose a self questionanswering model and a bidirectional mrc model for absa, but they can not solve all three ae, sc and aesc tasks. for prompt learning, many researchers regard it as a new learning paradigm along with the swift growth of prlm and argue that it can effectively reel off the enlightenment of prlm. @xcite have applied prompt learning to named entity recognition and sentiment analysis respectively. @xcite implement a sentence classification model by prompt learning and logic rules. on the selection and generation of prompt, @xcite propose an automatic prompt generation method and @xcite put forward a p-tuning idea to improve the effectiveness of prompt. however, the above works do not pay attention to the query generation of mrc and the sparsity of the verbalizer of prompt learning. in this paper, we propose a mrcool framework for absa which designs a query encoder to improve the capability of mrc and a simple mlp verbalizer is used to reduce the sparsity of prompt learning. ## conclusion in this paper, we propose a mrcool framework to handle ae, sc and aesc subtasks of absa in one shot through the process of mrc with prompt learning. in detail, we first model aspect extraction as an mrc task and then let the mrc module help aspect-level sentiment classification implemented in a prompt learning way so that we present an end-to-end framework to fulfill the complete task requirement of absa. the experimental results demonstrate the effectiveness of our proposed framework by providing consistent and general performance improvement over strong baselines. in detail, our framework attains new state-of-the-art for ae subtask by considerable margins over previous methods on three datasets and reaches state-ofthe-art performance for aesc on two datasets.