Bin Liang


pdf bib
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge
Bin Liang | Hang Su | Rongdi Yin | Lin Gui | Min Yang | Qin Zhao | Xiaoqi Yu | Ruifeng Xu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.

pdf bib
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
Jianzhu Bao | Bin Liang | Jingyi Sun | Yice Zhang | Min Yang | Ruifeng Xu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.


pdf bib
结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)
Qinglin Zhu (祝清麟) | Bin Liang (梁斌) | Liuyu Han (刘宇瀚) | Yi Chen (陈奕) | Ruifeng Xu (徐睿峰) | Ruibin Mao (毛瑞彬)
Proceedings of the 19th Chinese National Conference on Computational Linguistics


pdf bib
基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)
Wangda Luo (骆旺达) | Yuhan Liu (刘宇瀚) | Bin Liang (梁斌) | Ruifeng Xu (徐睿峰)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

针对问答立场任务中,现有方法难以提取问答文本间的依赖关系问题,本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式,基于交互注意力机制和循环迭代方法,有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外,该方法将问题进行陈述化表示,有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明,本文方法取得了比现有模型方法更好的效果,同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。

pdf bib
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis
Bin Liang | Rongdi Yin | Lin Gui | Jiachen Du | Ruifeng Xu
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.


pdf bib
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis
Bin Liang | Jiachen Du | Ruifeng Xu | Binyang Li | Hejiao Huang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.