# Multitasking Framework for Unsupervised Simple Definition Generation

arXiv: https://arxiv.org/abs/2203.12926 · HTML: https://mlsanity.com/paper/2203.12926/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Hooks in the Headline: Learning to Generate Headlines with Controlled Styles](https://mlsanity.com/paper/2004.01980/) (2020)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Dynamic Multi-Level Multi-Task Learning for Sentence Simplification](https://mlsanity.com/paper/1806.07304/) (2018)
- [Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling](https://mlsanity.com/paper/1911.05715/) (2019)
- [MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases](https://mlsanity.com/paper/2005.00352/) (2020)
- [Incorporating Sememes into Chinese Definition Modeling](https://mlsanity.com/paper/1905.06512/) (2019)
- [VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling](https://mlsanity.com/paper/2010.03124/) (2020)
- [Breaking Sticks and Ambiguities with Adaptive Skip-gram](https://mlsanity.com/paper/1502.07257/) (2015)
- [Sentence Simplification with Memory-Augmented Neural Networks](https://mlsanity.com/paper/1804.07445/) (2018)
- [xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks](https://mlsanity.com/paper/1809.03348/) (2018)
- [Toward Cross-Lingual Definition Generation for Language Learners](https://mlsanity.com/paper/2010.05533/) (2020)

## Built on by (most cited first, 18 of 18)

- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [Fine-grained Contrastive Learning for Definition Generation](https://mlsanity.com/paper/2210.00543/) (2022)
- [A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models](https://mlsanity.com/paper/2403.07322/) (2024)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [Understanding Multimodal Deep Neural Networks: A Concept Selection View](https://mlsanity.com/paper/2404.08964/) (2024)
- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Models](https://mlsanity.com/paper/2404.10306/) (2024)
- [Can Large Language Models Understand Internet Buzzwords Through User-Generated Content](https://mlsanity.com/paper/2505.15071/) (2025)
- [Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions](https://mlsanity.com/paper/2507.11981/) (2025)
- [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)
- [CLIX: Cross-Lingual Explanations of Idiomatic Expressions](https://mlsanity.com/paper/2501.03191/) (2025)
- [MCTS: A Multi-Reference Chinese Text Simplification Dataset](https://mlsanity.com/paper/2306.02796/) (2023)
- [SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning](https://mlsanity.com/paper/2508.15471/) (2025)
- [LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts](https://mlsanity.com/paper/2602.14060/) (2026)

## Abstract (arXiv)

The definition generation task can help language learners by providing explanations for unfamiliar words. This task has attracted much attention in recent years. We propose a novel task of Simple Definition Generation (SDG) to help language learners and low literacy readers. A significant challenge of this task is the lack of learner's dictionaries in many languages, and therefore the lack of data for supervised training. We explore this task and propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts. We disentangle the complexity factors from the text by carefully designing a parameter sharing scheme between two decoders. By jointly training these components, the framework can generate both complex and simple definitions simultaneously. We demonstrate that the framework can generate relevant, simple definitions for the target words through automatic and manual evaluations on English and Chinese datasets. Our method outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%.