S pan M lt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020)

Abstract: Aspect terms extraction and opinion terms extraction are two key problems of fine-grained Aspect Based Sentiment Analysis (ABSA). The aspect-opinion pairs can provide a global profile about a product or service for consumers and opinion mining systems. However, traditional methods can not directly output aspect-opinion pairs without given aspect terms or opinion terms. Although some recent co-extraction methods have been proposed to extract both terms jointly, they fail to extract them as pairs. To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE). Furthermore, this paper treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works. We propose a multi-task learning framework based on shared spans, where the terms are extracted under the supervision of span boundaries. Meanwhile, the pair-wise relations are jointly identified using the span representations. Extensive experiments show that our model consistently outperforms state-of-the-art methods.

## introduction fine-grained aspect-based sentiment analysis (absa) or opinion mining is a field of study that analyzes people's detailed insights towards a product or service. aspect terms (at) extraction and opinion terms (ot) extraction are two fundamental subtasks in absa @xcite . aspect terms, also named as opinion targets, are the word sequences in the sentence describing attributes or features of the targets. opinion terms, sometimes called opinion words, are those expressions carrying subjective attitudes. for example, in the sentence "otherwise, this place has great service and prices and a nice friendly atmosphere", the aspect terms are service, prices and atmosphere, and the opinion terms are great and nice friendly. recently, a new research focus, which aims at co-extracting the aspect and opinion terms @xcite @xcite @xcite , has drawn increasing attention in both academia and industry. such methods use joint models and have achieved great progress on both subtasks. however, the extracted at and ot are not in pairs, and the corresponding relations between them are not well extracted. as the example sentence shown in figure 1 , (service, great), (prices, great) and (atmosphere, nice friendly) are three aspect-opinion pairs. in contrast, the co-extraction methods can only output the at set {service, prices, atmosphere} and the ot set {great, nice friendly} jointly. the aspect-opinion pairs can deploy more finegrained sentiment analysis for review text and will benefit many downstream applications, such as opinion summarization and product profiling. by referring to the aspect-opinion pairs in a review sentence, customers can get a glimpse of the pros and cons of a product or service in a short time. based on the promising results in previous at and ot extraction, one possible solution for aspect-opinion pair extraction is to decouple the whole task into two subtasks. firstly, all aspect terms need to be extracted from the sentences. then, the ot cor-responding to each at can be extracted using a target-oriented opinion words extraction (towe) method @xcite . though this two-stage pipeline approach can extract aspect-opinion pairs, it will suffer from error propagation and the pairs extracting performance will rely heavily on the accuracy of at extraction. to this end, an end-to-end method that can automatically extract at and ot as pairs is essential for fine-grained sentiment analysis and opinion mining. considering the significance of the aspectopinion pairs in review sentences, this paper targets at a new subtask for fine-grained absa, named paote (pair-wise aspect and opinion terms extraction). given a review sentence, the objective of paote is to extract all the (at, ot) pairs. different from the traditional co-extraction task of at and ot, paote outputs at and ot in pairs while the co-extraction task only outputs them in separate sets as shown in figure 1 . most of the previous at and ot extraction methods formulate the task as a sequence tagging problem @xcite @xcite , specifically using a 5-class tag set: {ba (beginning of aspect), ia (inside of aspect), bp (beginning of opinion), ip (inside of opinion), o (others)}. however, the sequence tagging methods suffer from a huge search space due to the compositionality of labels for extractive absa tasks, which has been proven in @xcite . and as the example in figure 1 , the sequence tagging methods get into trouble when there exist one-to-many or many-to-one relations between at and ot in the sentence. in this paper, we propose a span-based multi-task framework to jointly extract both the at/ot and the pair-wise relations. motivated by prior works @xcite , the proposed framework firstly learns word-level representations using a base encoder and then enumerates all possible spans on the input sentence. by sharing the generated span representations, the at/ot can be extracted under the supervision of span boundaries and class labels. meanwhile, the pair-wise relations can be identified by computing the span-span correspondence. we further design different encoder structures for the framework. to validate the effectiveness of our method, we conduct a serial of experiments based on public datasets. the comparison results show that the proposed framework can efficiently avoid the cascading errors between tasks and outperforms the state-of-the-art pipeline and joint methods. in summary, the main contributions of this paper are concluded as follows: ## conclusion in this paper, we study a novel task pair-wise aspect and opinion terms extraction (paote). we treat this task as a joint term and relation extraction problem and develop a span-based multi-task learning framework (spanmlt). our framework can effectively learn contextualized information with various base encoders. specifically, we try two different encoders (bilstm encoder and bert encoder). then a span generator enumerates all possible spans and each span is represented based on the outputs of the encoders. for joint optimizing the objectives of term extraction and pair-wise relation extraction, the two subtasks share the span representations and the losses are combined. the experimental results demonstrate that our spanmlt significantly outperforms all the compared methods. for future works, we will explore pair-wise at and ot extraction together with aspect category and sentiment polarity classification.