# Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling

arXiv: https://arxiv.org/abs/1911.05715 · HTML: https://mlsanity.com/paper/1911.05715/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 15 of 15)

- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Multitasking Framework for Unsupervised Simple Definition Generation](https://mlsanity.com/paper/2203.12926/) (2022)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)
- [Can Large Language Models Understand Internet Buzzwords Through User-Generated Content](https://mlsanity.com/paper/2505.15071/) (2025)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation](https://mlsanity.com/paper/2209.14614/) (2022)
- [Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling](https://mlsanity.com/paper/2305.14717/) (2023)
- [Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss](https://mlsanity.com/paper/2305.17855/) (2023)
- [LitMind Dictionary: An Open-Source Online Dictionary](https://mlsanity.com/paper/2204.11087/) (2022)
- [WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset](https://mlsanity.com/paper/2308.03582/) (2023)

## Abstract (arXiv)

Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations. Building on the distributional hypothesis, we argue here that the most natural formalization of definition modeling is to treat it as a sequence-to-sequence task, rather than a word-to-sequence task: given an input sequence with a highlighted word, generate a contextually appropriate definition for it. We implement this approach in a Transformer-based sequence-to-sequence model. Our proposal allows to train contextualization and definition generation in an end-to-end fashion, which is a conceptual improvement over earlier works. We achieve state-of-the-art results both in contextual and non-contextual definition modeling.