A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.

## introduction aspect based sentiment analysis (absa), also known as target based sentiment analysis, has received widespread attention in both academia and industry in recent years. absa allows a sentiment analysis of different aspects in a given sentence, which can be applied in many areas, such as social media and e-commerce reviews. compared with sentence sentiment analysis, absa is more fine-grained and more in line with reality. absa contains many sub-tasks, such as aspect term extraction (ate) @xcite , opinion term extraction (ote) @xcite , aspect level sentiment classification (alsc) @xcite , aspect sentiment pair extraction (aspe) @xcite @xcite , aspect opinion co-extraction (aoce) @xcite , aspect opinion pair extraction (aope) @xcite and aspect sentiment triplet extraction (aste) @xcite . good service but poor taste aspect term extration: {service, taste} opinion term extraction: {good, poor} aspect sentiment pair extraction: {(service, pos), (taste, neg)} aspect opinion co-extraction: {service, good, taste, poor} aspect opinion pair extraction: {(service, good), (taste, poor)} aspect sentiment triplet extraction: {(service, good, pos), (taste, poor, neg)} although researches have been conducted in the area, aste still faces many challenges: to address the above challenges, we propose a multi-task dual-tree network for aste, namely mtdtn. the constituency tree and dependency tree are two parsing methods of a sentence in natural language processing (nlp), and the latter has been widely used in absa tasks @xcite . although the two trees of one sentence can be transformed into each other, it may require hops over the structure in graph neural networks or self-attentions. thus our model employs both types of trees for aoce and aste, respectively. the constituency tree is applied in the coextraction module to detect constituent boundaries, and the dependency tree is applied in the triplet extraction module to capture relations between aspects and opinions. moreover, for the reason that different layers of bert @xcite capture hierarchical features, with surface features in lower layers, syntactic features in middle layers and semantic features in higher layers @xcite , we employ self-attention weights of the middle layer to modify the dependency graph. the modified graph can reduce inevitable parsing errors and imply more accurate relations between words. finally, we use a similar tagging scheme as the grid tagging scheme @xcite for triplet extraction and design a transition-based inference strategy (tbis) to transfer the boundary information from the co-extraction module to the triplet extraction module. the contributions of our work can be summarized as follows: ## related work aspect-opinion co-extraction (aoce) has been focused on in recent years, aiming to explore the interactions between aspect term extraction (ate) and opinion term extraction (ote). initially, models have been proposed to co-extract aspects and opinions in a sentence, treating the task as a sequence labeling problem @xcite @xcite . however, they do not consider the relations between corresponding aspects and opinions. then @xcite define the aspect opinion pair extraction (aope) task and propose a span-based multi-task learning framework. @xcite propose a synchronous double-channel recurrent network to obtain aspect-opinion pairs and achieve great performance. to further explore the interactions between paired terms and sentiment polarity, @xcite first define the task of aspect-sentiment triplet extraction (aste) and propose a two-stage model to address it. following this work, a positionaware tagging scheme @xcite ) and a grid tagging scheme @xcite are designed to jointly extract the triplets in an end-to-end manner. @xcite further represent the semantic and syntactic relations between word pairs by a graph to enhance the vanilla grid tagging scheme. interactions between aspect spans and opinion spans are also studied to not only consider word-to-word interactions @xcite . @xcite transform the triplet extraction task into a machine reading comprehension (mrc) task with well-designed queries. ## task definition given an input sentence @xmath0, x 2 , • • • , x n } of length n, we then formulate two sub-tasks as two different sequence labeling problems. ## conclusions in this paper, we propose a multi-task framework for aspect sentiment triplet extraction (aste) with aspect opinion co-extraction (aoce) as an auxiliary task. the two sub-tasks utilize two types of trees to capture different information of the text. for a given sentence, a constituency tree is employed by a graph convolution network for aoce, and a modified dependency tree is employed by a special transformer decoder for aste. we further designed a transition-based inference strategy (tbis) to enhance information interaction between sub-tasks by transferring the boundary information from aoce to aste through a transition matrix. the whole model is called multi-task dual-tree network (mtdtn) and extensive experiments demonstrate that our model achieves stateof-the-art performance on four popular datasets.