Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Invited Talk: Generationary or: “How We Went beyond Sense Inventories and Learned to Gloss”
In this talk I present Generationary, an approach that goes beyond the mainstream assumption that word senses can be represented as discrete items of a predefined inventory, and put forward a unified model which produces…
DecoderWord Sense DisambiguationC2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition
Continuous Sign Language Recognition (CSLR) aims to transcribe the signs of an untrimmed video into written words or glosses. The mainstream framework for CSLR consists of a spatial module for visual representation l…
Language ModellingRepresentation LearningSign Language RecognitionVCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling
In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexica…
Variational InferenceWord EmbeddingsGlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings
Consulting a dictionary or a glossary is a familiar way for many humans to figure out what does a word in a particular context mean. We hypothesize that a system that can select a proper definition for a particular word …
Cross-Lingual TransferLanguage ModellingTask 2Word Embeddings+3BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders
This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which consists in ge…
PositionReverse DictionaryWord Embeddings