Definition Modelling for Appropriate Specificity
Han Huang, Tomoyuki Kajiwara, Yuki Arase · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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/underspecificity 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 encoderdecoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. 1 Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-theart method.Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.