Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis (2023)

Abstract: Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts:aspect term, aspect category, opinion term, and sentiment polarity. Most computational approaches focus on some of the ABSA sub-taskssuch as tuple (aspect term, sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity) extraction using either pipeline or joint modeling approaches. Recently, generative approaches have been proposed to extract all four elements as (one or more) quadrupletsfrom text as a single task. In this work, we take a step further and propose a unified framework for solving ABSA, and the associated sub-tasksto improve the performance in few-shot scenarios. To this end, we fine-tune a T5 model with instructional prompts in a multi-task learning fashion covering all the sub-tasks, as well as the entire quadruple prediction task. In experiments with multiple benchmark datasets, we show that the proposed multi-task prompting approach brings performance boost (by absolute 8.29 F1) in the few-shot learning setting.

## introduction aspect-based sentiment analysis (absa) is a fine-grained sentiment analysis task where the goal is to extract the sentiment associated with an entity and all its aspects @xcite @xcite @xcite @xcite . for example, in the context of restaurant reviews the relevant aspects could be food, ambience, location, service with general used to represent the subject itself (i.e., restaurant). absa can provide valuable fine-grained information for businesses to analyze the aspects they care about. annotated datasets have been released to foster research in this area @xcite @xcite . a full absa task aims to extract four elements from a user-generated text: aspect term, aspect category, opinion term and the sentiment polarity (see figure 1 for an example). most existing approaches have the focus on extracting some of these elements such as a single element (e.g., aspect term), tuple (e.g., aspect term, sentiment polarity), or triplet (e.g., aspect term, aspect category, sentiment polarity) @xcite @xcite . recently, @xcite tackled the full absa task, under the name of aspect sentiment quadruple prediction (asqp). technically, most existing computational approaches have used extractive and discriminative models either in a pipeline or in an end-to-end framework @xcite @xcite to address absa. generative approaches have been recently shown to be effective for the full absa task and its sub-tasks @xcite . most notably, @xcite used a sequence-to-sequence (seq-to-seq) model to address asqp as a paraphrase generation problem. one important consideration is that modeling absa in a generative fashion allows for cross-task knowledge transfer. we go a step further and propose a unified model that can tackle multiple absa sub-tasks, including the asqp task, and explore its effectiveness for low data scenarios. recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions and that models trained on these instructions show strong zero-shot performance on several tasks @xcite . based on this success, we propose a unified model based on multi-task prompting with instructional prompts using t5 @xcite to solve the full absa task i.e., asqp @xcite and several of its associated sub-tasks addressed in the literature: 1) aspect term extraction (ae) @xcite ; 2) aspect term extraction and sentiment classification (aesc) @xcite ; 3) target aspect sentiment detection (tasd), which aims to extract the aspect term, aspect category, and sentiment polarity @xcite ; 4) aspect sentiment triplet extraction (aste), which aims to extract the aspect term, opinion term, sentiment polarity @xcite . we conduct an extensive set of experiments with multiple review datasets. experimental results show that our proposed model achieves substantial improvement (8.29 f1 on average) against the state-of-the-art in few-shot learning scenario 1 . ## methods the four elements of absa form a quadruple as the sentiments are associated with both the aspect, and the opinion terms (cf figure 1 ). in this work, we hypothesize that it is important to capture the interaction between these components not only at the quadruple level, but also within a subset of these four elements. we consider multiple factorized sub-tasks involving one or more of the four elements to be predicted. we pose it as a combination of five question answering (qa) tasks as illustrated in figure 2 . for each qa task, an instructional prompt is used to train a seq-to-seq model to learn one or more absa elements -referred to as instruction tuning (it). our formulation enables learning all sub-tasks via multi-task learning (mtl). ## experiments as this work is one of the first few attempts towards studying few-shot learning in absa context, unsurprisingly, there is a lack of standard few-shot datasets. we emulate few-shot data drawing inspiration from the literature @xcite for our experiments. ## conclusion in this paper, we posed absa as an instruction tuning based seq-to-seq modeling task. we factorized the overall quadruple prediction task into five