Towards Generative Aspect-Based Sentiment Analysis (2021)

Abstract: Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional task-specific model design.

## introduction aspect-based sentiment analysis (absa), aiming at mining fine-grained opinion information towards specific aspects, has attracted increasing attention in recent years @xcite . multiple fundamental sentiment elements are involved in absa, including the aspect term, opinion term, aspect category, and sentiment polarity. given a simple example sentence "the pizza is delicious.", the corresponding elements are "pizza", "delicious", "food quality" and "positive", respectively. the main research line of absa focuses on the identification of those sentiment elements such as extracting the aspect term @xcite @xcite or classifying the sentiment polarity for a given aspect @xcite @xcite . to provide more detailed information, many recent studies propose to jointly predict multiple elements simultaneously @xcite @xcite . taking the unified absa (uabsa, also called end-to-end absa) task as an example, it tries to simultaneously predict the mentioned aspect terms and the corresponding sentiment polarities @xcite . in general, most absa tasks are formulated as either sequence-level or token-level classification problems @xcite . by designing taskspecific classification networks, the prediction is made in a discriminative manner, using the class index as labels for training @xcite . however, these methods ignore the label semantics, i.e., the meaning of the natural language labels, during the training process. intuitively, knowing the meaning of "food quality" and "restaurant ambiance", it can be much easier to identify that the former one is more likely to be the correct aspect category for the concerned aspect "pizza". such semantics of the label can be more helpful for the joint extraction of multiple sentiment elements, due to the complicated interactions of those involved elements. for example, understanding "delicious" is an adjective for describing the food such as "pizza" could better lead to the prediction of aspect opinion pair ("pizza", "delicious"). another issue is that different classification models are proposed to suit the need of different absa problems, making it difficult to adapt the model from one to another. motivated by recent success in formulating sev-eral language understanding problems such as named entity recognition, question answering, and text classification as generation tasks @xcite , we propose to tackle various absa problems in a unified generative approach in this paper. it can fully utilize the rich label semantics by encoding the natural language label into the target output. moreover, this unified generative model can be seamlessly adapted to multiple tasks without introducing additional task-specific model designs. in order to enable the generative aspect-based sentiment analysis (gas), we tailor-make two paradigms, namely annotation-style and extractionstyle modeling to transform the original task as a generation problem. given a sentence, the former one adds annotations on it to include the label information when constructing the target sentence; while the latter directly adopts the desired natural language label of the input sentence as the target. the original sentence and the target sentence produced by either paradigm can then be paired as a training instance of the generation model. furthermore, we propose a prediction normalization strategy to handle the issue that the generated sentiment element falls out of its corresponding label vocabulary set. we investigate four absa tasks including aspect opinion pair extraction (aope), unified absa (uabsa), aspect sentiment triplet extraction (aste), and target aspect sentiment detection (tasd) with the proposed unified gas framework to verify its effectiveness and generality. our main contributions are 1) we tackle various absa tasks in a novel generative manner; 2) we propose two paradigms to formulate each task as a generation problem and a prediction normalization strategy to refine the generated outputs; 3) we conduct experiments on multiple benchmark datasets across four absa tasks and our approach surpasses previous state-of-the-art in almost all cases. specifically, we obtain 7.6 and 3.7 averaged gains on the challenging aste and tasd task respectively. ## conclusions and future work we tackle various absa tasks in a novel generative framework in this paper. by formulating the target sentences with our proposed annotation-style and extraction-style paradigms, we solve multiple sentiment pair or triplet extraction tasks with a unified generation model. extensive experiments on multiple benchmarks across four absa tasks show the effectiveness of our proposed method. our work is an initial attempt on transforming absa tasks, which are typically treated as classification problems, into text generation problems. experimental results indicate that such transformation is an effective solution to tackle various absa tasks. following this direction, designing more effective generation paradigms and extending such ideas to other tasks can be interesting research problems for future work.