Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis (2020)

Abstract: Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is “delicious” , the aspect term must be “food” rather than “place” . In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.

## introduction aspect-based sentiment analysis (absa) is a finegrained task which aims to summarize the opinions of users towards specific aspects in a sentence. absa normally involves three subtasks, namely aspect term extraction (ae), opinion term extraction (oe), and aspect-level sentiment classification (sc). for example, given a review "the place is small and cramped but the food is delicious.", ae aims to extract a set of aspect terms {"place", "food"}. oe aims to extract a set of opinion terms {"small", "cramped", "delicious"}. meanwhile, it is expected for sc to assign a sentiment polarity "negative" and "positive" to the aspect "place" and "food", respectively. *corresponding author. most existing works treat absa as a two-step task containing ae and sc. they develop one separate method for each subtask @xcite @xcite , or take oe as an auxiliary task of ae @xcite . in order to perform absa for practical use, the separate methods need to be pipelined together. recently, several studies attempt to solve absa in a unified framework @xcite @xcite . despite their effectiveness, we argue that these methods are not sufficient to yield satisfactory performance for the complete absa task. the key reason is that the interactive relations among different subtasks have been largely neglected in existing studies. these relations convey collaborative signals which can enhance the subtasks in a mutual way. for example, the opinion term "delicious" can serve as the evidence of the aspect term "food", and vice versa. in the following, we first analyze the interactive relations among different subtasks, and then present our racl framework which is developed to exploit these relations. the detailed relations are summarized in figure 1 (left), where each arrow ⇔ denotes one specific relation r i . when reviewing the literature on the absa task, we find that existing separate methods either do not utilize any relations, or only utilize r 1 by treating oe as an auxiliary task of ae. meanwhile, the unified methods at most explicitly utilize r 3 and r 4 . in view of this, we propose a novel relation-aware collaborative learning (racl) framework to fully exploit the interactive relations in the complete absa task. we compare our model with existing methods by their capability in utilizing interactive relations in racl is a multi-layer multi-task learning framework with a relation propagation mechanism to mutually enhance the performance of subtasks. for multi-task learning, racl adopts the sharedprivate scheme @xcite . subtasks ae, oe, and sc first jointly train the low-level shared features, and then they train their high-level private features independently. in this way, the shared and private features can embed the task-invariant and task-oriented knowledge respectively. for relation propagation, racl improves the model capacity by exchanging informative clues among three subtasks. moreover, racl can be stacked to multiple layers to perform collaborative learning at different semantic levels. we conduct extensive experiments on three datasets. results demonstrate that racl significantly outperforms the state-of-the-art methods for both the single subtasks and the complete absa task. ## related work aspect-based sentiment analysis (absa) is first proposed by @xcite and has been widely studied in recent years @xcite . we organize existing studies by how the subtasks are performed and combined to perform absa. separate methods most existing studies treat absa as a two-step task containing aspect term extraction (ae) and aspect-based sentiment classification (sc), and develop separate methods for ae @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite , and sc @xcite @xcite @xcite @xcite @xcite , respectively. some of them resort to the auxiliary task opinion term extraction (oe) and exploit their relation for boosting the performance of ae. for the complete absa task, results from two steps must be merged together in a pipeline manner. in this way, the relation between ae/oe and sc is totally neglected, and the errors from the upstream ae/oe will be propagated to the downstream sc. the overall performance of absa task is not promising for pipeline methods. unified methods recently, several studies attempt to solve absa task in a unified framework. the unified methods fall into two types: collapsed tagging @xcite @xcite and joint training @xcite . the former combines the labels of ae and sc to construct collapsed labels like {b-senti, i-senti, o}. the subtasks need to share all trainable features without distinction, which is likely to confuse the learning process. moreover, the relations among subtasks cannot be explicitly modeled for this type of methods. meanwhile, the latter constructs a multi-task learning framework where each subtask has independent labels and can have shared and private features. this allows the interactive relations among different subtasks to be modeled explicitly for the joint training methods. however, none of existing studies along this line has fully exploited the power of such relations. we differentiate our work from aforementioned methods in that we propose a unified framework which exploits all dyadic and triadic relations among subtasks to enhance the learning capability. ## analysis 5.1 ablation study to investigate the effects of different relations on racl -glove/-bert, we conduct the following ablation study. we sequentially remove each interactive relation and obtain four simplified variants. as expected, all simplified variants in