Ruibin Mao


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Context or Knowledge is Not Always Necessary: A Contrastive Learning Framework for Emotion Recognition in Conversations
Geng Tu | Bin Liang | Ruibin Mao | Min Yang | Ruifeng Xu
Findings of the Association for Computational Linguistics: ACL 2023

Emotion recognition in conversations (ERC) aims to detect the emotion of utterances in conversations. Existing efforts generally focus on modeling context- and knowledge-sensitive dependencies. However, it is observed that the emotions of many utterances can be correctly detected without context or external knowledge. In such cases, blindly leveraging the context and external knowledge may impede model training. Based on this, we propose a novel framework based on contrastive learning (CL), called CKCL (including the contrastive learning scenarios among Context and Knowledge), to distinguish the above utterances for better vector representations. The CKCL framework defines context- and knowledge-independent utterances, as the positive sample, whose predicted results are unchanged even masking context and knowledge representations, otherwise, the negative sample. This can obtain a latent feature reflecting the impact degree of context and external knowledge on predicted results, thus effectively denoising irrelevant context and knowledge during training. Experimental results on four datasets show the performance of CKCL-based models is significantly boosted and outperforms state-of-the-art methods.


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The Design and Construction of a Chinese Sarcasm Dataset
Xiaochang Gong | Qin Zhao | Jun Zhang | Ruibin Mao | Ruifeng Xu
Proceedings of the Twelfth Language Resources and Evaluation Conference

As a typical multi-layered semi-conscious language phenomenon, sarcasm is widely existed in social media text for enhancing the emotion expression. Thus, the detection and processing of sarcasm is important to social media analysis. However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset. In this paper, we presents the design and construction of a largest high-quality Chinese sarcasm dataset, which contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcastic texts. Furthermore, a balanced dataset through elaborately sampling the same amount non-sarcastic texts for training sarcasm classifier. Using the dataset as the benchmark, some sarcasm classification methods are evaluated.

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Target-based Sentiment Annotation in Chinese Financial News
Chaofa Yuan | Yuhan Liu | Rongdi Yin | Jun Zhang | Qinling Zhu | Ruibin Mao | Ruifeng Xu
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper presents the design and construction of a large-scale target-based sentiment annotation corpus on Chinese financial news text. Different from the most existing paragraph/document-based annotation corpus, in this study, target-based fine-grained sentiment annotation is performed. The companies, brands and other financial entities are regarded as the targets. The clause reflecting the profitability, loss or other business status of financial entities is regarded as the sentiment expression for determining the polarity. Based on high quality annotation guideline and effective quality control strategy, a corpus with 8,314 target-level sentiment annotation is constructed on 6,336 paragraphs from Chinese financial news text. Based on this corpus, several state-of-the-art sentiment analysis models are evaluated.

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基于BERTCA的新闻实体与正文语义相关度计算模型(Semantic Relevance Computing Model of News Entity and Text based on BERTCA)
Junyi Xiang (向军毅) | Huijun Hu (胡慧君) | Ruibin Mao (毛瑞彬) | Maofu Liu (刘茂福)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

目前的搜索引擎仍然存在“重形式,轻语义”的问题,无法做到对搜索关键词和文本的深层次语义理解,因此语义检索成为当代搜索引擎中亟需解决的问题。为了提高搜索引擎的语义理解能力,提出一种语义相关度的计算方法。首先标注金融类新闻标题实体与新闻正文语义相关度语料1万条,然后建立新闻实体与正文语义相关度计算的BERTCA(Bidirectional Encoder Representation from Transformers Co-Attention)模型,通过使用BERT预训练模型,综合考虑细粒度的实体和粗粒度的正文的语义信息,然后经过协同注意力,实现实体与正文的语义匹配,不仅能计算出金融新闻实体与新闻正文之间的相关度,还能根据相关度阈值来判定相关度类别,实验表明该模型在1万条标注语料上准确率超过95%,优于目前主流模型,最后通过具体搜索示例展现该模型的优秀性能。

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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



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A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Chuang Fan | Hongyu Yan | Jiachen Du | Lin Gui | Lidong Bing | Min Yang | Ruifeng Xu | Ruibin Mao
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure.