LEGO - ABSA : A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022)

Abstract: Aspect-based sentiment analysis (ABSA) has received increasing attention recently. ABSA can be divided into multiple tasks according to the different extracted elements. Existing generative methods usually treat the output as a whole string rather than the combination of different elements and only focus on a single task at once. This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts. Further, the proposed approach can train on simple tasks and transfer to difficult tasks by assembling task prompts, like assembling Lego bricks. We conduct experiments on six ABSA tasks across multiple benchmarks. Our proposed multi-task approach achieves new state-of-the-art results in almost all tasks and competitive results in task transfer scenarios.

## introduction absa is a fine-grained sentiment analysis task that has attracted increasing attention in recent years @xcite . absa aims to extract different elements including: 1) the aspect term(a); 2) opinion term(o); 3) the aspect category(c) corresponding to the aspect term; 4) the sentiment polarity(s) for a specific aspect term. for example, in the sentence "pizza is delicious", "pizza" is an aspect term belonging to the food category, and the corresponding opinion term is "delicious", which expresses positive sentiment. as shown in this paper explores tasks containing two or more elements. in general, most absa tasks are transferred to classification tasks. previous works often ## conclusion in this paper, we propose a prompt-based generative framework lego-absa for absa tasks that use t5 as the backbone, which can make full use of the information learned from the t5 unsupervised training object through the formulation of task prompts we proposed. lego-absa does not regard the prompt and the output text as a simple string but a combination of multiple elements to be extracted. it is mainly used in multi-task training and task transfer scenarios. extensive experiments on six absa tasks verify the effectiveness of our framework and its excellent transferability in task transfer scenarios. there is still space for improvement in our framework, such as completing the combination extraction of multiple elements task through the learning of single element tasks.