Definition Modelling for Appropriate Specificity
Definition Modelling for Appropriate Specificity
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DOI：
10.18653/v1/2021.emnlp-main.194
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Han Huang-;Tomoyuki Kajiwara;Yuki Arase
中科院分区：
文献类型：
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作者：
Han Huang-;Tomoyuki Kajiwara;Yuki Arase
定义生成技术旨在生成给定上下文的目标单词或短语的定义。在以往的研究中，研究人员面临着词汇外问题和特异性过/不足问题等各种问题。过于具体的定义表示狭窄的单词含义，而不具体的定义表示一般的和上下文不敏感的含义。在此，我们提出了一种具有适当特异性的定义生成方法。该方法利用预先训练的编码器-解码器模型（即文本到文本传输转换器）解决了上述问题，并引入了重新排序机制以实现定义中的模型特异性。在标准评估数据集上的实验结果表明，我们的方法明显优于以前的最先进的方法。此外，手工评估证实了我们的方法有效地解决了特异性过高/不足的问题。
Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.