RDMTL: Reverse dictionary model based on multitask learning

Abstract

A reverse dictionary is a task of finding appropriate words based on given definitions. Existing studies struggle to capture the subtle differences between words with similar definitions. Motivated by humans’ ability to verify their understanding of a word's semantics by making example sentences and recognizing parts of speech, we propose a reverse dictionary model based on multitask learning (RDMTL), which can alleviate the problem. RDMTL comprises a primary task component that extracts different levels of semantic features from definitions and two auxiliary task components that predict part-of-speech tags and generate sentences for target words. Through jointly learning these three tasks, RDMTL can enhance the understanding of definitions and discover subtle differences among words with similar meanings. Moreover, it can generate more accurate and natural example sentences. We evaluate RDMTL on a modified version of the New Oxford dataset and compare its performance with several baseline models. The experimental results show that RDMTL enhances the rank value metric for the reverse dictionary task by 1.02%, the F1 value metric for the part-of-speech classification task by 20.47%, and the SB-4 metric for the sentence generation task by 14.7%. In addition, to analyze the contribution of each component and the impact of multitask learning, we conducted an ablation study. In this study, a new method and perspective for the reverse dictionary task is introduced.