A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis (2022)

Abstract: Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to insufficient feature interactions, or they encode aspect features and sentiment features in a parallel manner, implying that feature representation in each task is largely independent of each other except for input sharing. Both of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately, where the hierarchical interactions involve two steps: shallow-level interaction and deep-level interaction. First, we utilize cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning between two tasks in the output layer, thus the aspect input and the sentiment input are capable of encoding features of the other task via backpropagation. Extensive experiments on three real-world datasets demonstrate HI-ASA’s superiority over baselines.

## introduction aspect-sentiment analysis (asa) @xcite aims at extracting all the aspects and their corresponding sentiments within the text simultaneously. and it can be divided into two tasks, i.e., aspect extraction (ae) and sentiment classification (sc). ae is to extract the aspects @xcite @xcite in the sentence, and sc aims to predict the sentiments @xcite @xcite for the extracted aspects. in recent years, the span-based models @xcite @xcite are increasingly becoming an alternative for asa because of their inherent advantages (e.g., they can avoid sentiment inconsistency and huge search space problems in tagging-based models @xcite ), where the aspects are extracted by directly predicting the boundary distributions, and the sentiment polarities are classified based on the aspect-level words. for example, in the sentence "the screen size is satisfactory but the phone battery capacity is limited.", the aspect spans are "screen size" and "the phone battery", and span-level sentiments are positive and negative, respectively. formally, most of the existing methods can be divided into two types: sequential encoding @xcite and parallel encoding @xcite due to different ways in encoding task-specific features. in sequential encoding, the task-specific features are extracted sequentially, i.e., features extracted later have no direct associations with previous ones, which is a unidirectional interaction strategy. in parallel encoding, task-specific features are extracted independently except for using shared input, i.e., the interaction is only present in input sharing. hence, both encoding methods above fail to model two-way interactions between ae and sc appropriately. in practice, the learning of the ae and sc may mutually influence each other. on the one hand, sentiment words can be understood better if given the desired aspects. for example, in the two sentences: "the battery has a large capacity." and "the computer case is too large to carry.", we can find that the sentiment word "large" expresses positive sentiment when describing the aspect "hard drive" while negative sentiment towards "computer". this actually shows that incorporating the aspect features is conducive to sc. on the other hand, since sentimental expressions are usually close to aspects, the potential sentiment features may provide useful signals for ae. for example, if we observe the word "spicy" appears in a restaurant review, it is likely to exist an aspect related to food. thus, incorporating the sentiment feature also facilitates aspect detection. these two examples illustrate that it is essential to reasonably establish the two-way associations between two tasks. inspired by the above analysis, in this work, we propose a novel hierarchical interactive model for joint span-based aspect-sentiment analysis (hi-asa). specifically, the hierarchical interactions are achieved in two steps: shallow-level interaction and deep-level interaction. the former learns semanticlevel interactive features to facilitate information sharing between two tasks in encoding layer. and the latter takes advantage of the technique of mutual information maximization to ensure two-way interaction between the two tasks. extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed methods. ## model in this section, we formally define the problem of joint span-based aspect-sentiment analysis. giving a training set {(x i , {(s j the overall architecture is illustrated in figure 1 , which mainly consists of two parts: ae and sc. we model the hierarchical interactions between two tasks appropriately to enhance the correlations. for the input section, firstly, we leverage bert @xcite to extract the semantic information for input sentence. the output through the transformer layers is @xmath0, b 2 , . . . , b n } ∈ r n×d , where d is the bert's embedding size, n is the length of sentence. then, we stack grus upon b for different tasks, here, we define the outputs of ae and sc as h 0 @xmath1, respectively, where d is the dimension size of gru. ## related work aspect-sentiment analysis (asa) @xcite is an essential task in sentiment analysis and can be separated into two tasks, i.e., aspect extraction (ae) and sentiment classification (sc). over the past years, some spanbased methods have achieved promising results for asa, which first extract aspects by detecting aspect boundaries (ae) and then predict the spanlevel sentiments (sc). ae has been widely studied by traditional machine @xcite and deep learning algorithms @xcite . however, the absence of sentiment information may result in redundant and noisy detection. sc is to predict the sentiment expressed on some specific aspects in a sentence, which has been studied extensively in nlp community @xcite @xcite . however, these aspects must be annotated before the ae task. @xcite proposed an extractthen-classify framework, which extracted aspects with a heuristic decoding algorithm and then correspondingly classified the span-level sentiments. @xcite designed share-private representation for each task to capture the correlations between two tasks in encoding layer. besides, @xcite introduced a joint model based on span-aware attention mechanism to predict the sentiment polarity. although achieved improved performances, these works fail to model two-way interactions between ae and sc appropriately. ## conclusion in this paper, we proposed a novel aspect-sentiment analysis model named hi-asa. the proposed model is equipped with a hierarchical interactive network to facilitate information sharing between the aspect extraction task and the sentiment classification task. the experimental results on three benchmark datasets demonstrated hi-asa's effectiveness and generality.