幽默识别是自然语言处理的新兴研究领域之一。对话的特殊结构使得在对话中的幽默识别相较于短文本幽默识别更具有挑战性。在对话中,除了当前话语以外,上下文语境信息对于幽默的识别也至关重要。因此,该文在已有研究的基础上结合对话的结构特征,提出基于BERT的强化语境与语义信息的对话幽默识别模型。模型首先使用BERT对发言人信息和话语信息进行编码,其次分别使用句级别的BiLSTM、CNN和Attention机制强化语境信息,使用词级别的BiLSTM和Attention机制强化语义信息。实验结果表明,该文方法能有效提升机器识别对话中幽默的能力。
Abstract
Humor recognition is one of the emerging research issues in natural language processing. The special structure of dialogue makes humor recognition in dialogue more challenging in that, in addition to the current utterance, contextual information is also crucial for humor recognition. This proposes a BERT-based model for humor recognition in dialogue with enhanced context and semantic information. The model uses BERT to encode the initial utterance and speaker information, then employs sentence-level BiLSTM, CNN and Attention mechanism to enhance contextual information, and character-level BiLSTM and Attention mechanism to enhance semantic information. The experimental result shows that the model proposed can effectively improve the performance of humor recognition in dialogue.
关键词
幽默识别 /
对话结构 /
BERT
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Key words
humor recognition /
dialogue structure /
BERT
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参考文献
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基金
国家自然科学基金(61602044,61772081)
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