Towards Non-Ambiguous Reverse Dictionary

Abstract

Reverse dictionary(RD) is to map a description or definition to a name of a concept. Most of the existing reverse dictionary methods are word-based, failing to handle the polysemy of words. In this paper, we propose Non-Ambiguous Reverse Dictionary(NARD), a synset-based model using synset embeddings to replace word embeddings. NARD provides different and effective embeddings for different senses in multi-sense words, allowing for a more convergent mapping of words and descriptive sentences at the semantic level. With a cross-dictionary sense tagging, NARD can utilize all word-based dictionaries for training. The pre-trained synset embeddings and the multi-task learning methods are adopted to improve the generalization performance of the model. NARD are trained on one-fifth of the synset-labeled English datasets, and achieves the new state-of-the-art results.