"Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics
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Abstract (arXiv)
Definition Modeling, the task of generating definitions, was first proposed as a means to evaluate the semantic quality of word embeddings-a coherent lexical semantic representations of a word in context should contain all the information necessary to generate its definition. The relative novelty of this task entails that we do not know which factors are actually relied upon by a Definition Modeling system. In this paper, we present evidence that the task may not involve as much semantics as one might expect: we show how an earlier model from the literature is both rather insensitive to semantic aspects such as explicit polysemy, as well as reliant on formal similarities between headwords and words occurring in its glosses, casting doubt on the validity of the task as a means to evaluate embeddings.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 46 references that are in the map. First posted on arXiv 14 Jun 2023.
Builds on
14 of 46 references.
References · 14
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
- 2016Definition Modeling: Learning to define word embeddings in natural languagecited by 46
- 2018Conditional Generators of Words Definitionscited by 35
- 2015Learning to Understand Phrases by Embedding the Dictionarycited by 33
- 2017Learning to Explain Non-Standard English Words and Phrasescited by 21
- 2022Multitasking Framework for Unsupervised Simple Definition Generationcited by 18
- 2022Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddingscited by 18
- 2019Mark my Word: A Sequence-to-Sequence Approach to Definition Modelingcited by 15
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakescited by 24
- 2024AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modelingcited by 3
- 2025Adapting Definition Modeling for New Languages: A Case Study on Belarusiancited by 0
- 2025Explaining novel senses using definition generation with open language modelscited by 0
- 2025Explaining novel senses using definition generation with open language models30 Sep 2025
- 2025Adapting Definition Modeling for New Languages: A Case Study on Belarusian13 Jul 2025
- 2024AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling4 Jul 2024
- 2024SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes12 Mar 2024