论文标题
反向字典和定义建模的统一模型
A Unified Model for Reverse Dictionary and Definition Modelling
论文作者
论文摘要
我们构建一个双向神经词典来检索给定定义的单词，并为查询单词制作定义。该模型同时学习这两个任务，并通过嵌入处理未知单词。它通过共享层对同一表示空间进行单词或定义，然后以多任务方式生成其他形式。我们的方法可以在没有额外资源的情况下在先前的基准测试上实现有希望的自动分数。人类注释者更喜欢模型在无参考和基于参考的评估中的输出，表明其实用性。分析表明，多个目标受益于学习。
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.
