Conditional Generators of Words Definitions

TL;DR

This paper improves word definition modeling by addressing ambiguity and polysemy using latent variables and attention, leading to better performance in generating accurate definitions.

Contribution

It introduces a novel approach combining latent variables and soft attention to handle word ambiguity in definition modeling.

Findings

Enhanced definition modeling accuracy with ambiguity handling

Latent variable and attention mechanisms improve performance

Qualitative analysis confirms better handling of polysemy

Abstract

We explore recently introduced definition modeling technique that provided the tool for evaluation of different distributed vector representations of words through modeling dictionary definitions of words. In this work, we study the problem of word ambiguities in definition modeling and propose a possible solution by employing latent variable modeling and soft attention mechanisms. Our quantitative and qualitative evaluation and analysis of the model shows that taking into account words ambiguity and polysemy leads to performance improvement.