Year: 2023
Type: preprint
Abstract: Contextualized embeddings are proven to be powerful tools in multiple NLP tasks. Nonetheless, challenges regarding their interpretability and capability to represent lexical semantics still remain. In this paper, we propose that the task of definition modeling, which aims to generate the human-readable definition of the word, provides a route to evaluate or understand the high dimensional semantic vectors. We propose a `Vec2Gloss' model, which produces the gloss from the target word's contextualized embeddings. The generated glosses of this study are made possible by the systematic gloss patterns provided by Chinese Wordnet. We devise two dependency indices to measure the semantic and contextual dependency, which are used to analyze the generated texts in gloss and token levels. Our results indicate that the proposed `Vec2Gloss' model opens a new perspective to the lexical-semantic applications of contextualized embeddings.
Source: arXiv (Cornell University)
Institution: National Taiwan University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4378770685