We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model's outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.
@article{arxiv.2205.04602,
title = {A Unified Model for Reverse Dictionary and Definition Modelling},
author = {Pinzhen Chen and Zheng Zhao},
journal= {arXiv preprint arXiv:2205.04602},
year = {2022}
}