mlsanityData to 7 Oct 2026

Fine-grained Contrastive Learning for Definition Generation

arXiv 2210.00543 ↗First posted 2 Oct 2022cs.CLcited by 10

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Abstract (arXiv)

Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG). However, previous encoder-decoder models lack effective representation learning to contain full semantic components of the given word, which leads to generating under-specific definitions. To address this problem, we propose a novel contrastive learning method, encouraging the model to capture more detailed semantic representations from the definition sequence encoding. According to both automatic and manual evaluation, the experimental results on three mainstream benchmarks demonstrate that the proposed method could generate more specific and high-quality definitions compared with several state-of-the-art models.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 2 Oct 2022.

Builds on

16 of 27 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  7. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,213
  8. 2021CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generationcited by 81
  9. 2016Definition Modeling: Learning to define word embeddings in natural languagecited by 46
  10. 2020PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generationcited by 42
  11. 2018Conditional Generators of Words Definitionscited by 35
  12. 2017Learning to Explain Non-Standard English Words and Phrasescited by 21
4 more
  1. 2022Multitasking Framework for Unsupervised Simple Definition Generationcited by 18
  2. 2019Incorporating Sememes into Chinese Definition Modelingcited by 13
  3. 2020VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modelingcited by 13
  4. 2018pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inferencecited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 1; 2026: 2.

10 papers.

  1. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
  2. 2023Multi-level Contrastive Learning for Script-based Character Understandingcited by 15
  3. 2023Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learningcited by 13
  4. 2024A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Modelscited by 5
  5. 2024Understanding Multimodal Deep Neural Networks: A Concept Selection Viewcited by 4
  6. 2024Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuningcited by 3
  7. 2024Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Modelscited by 3
  8. 2025SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuningcited by 0
  9. 2026LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Expertscited by 0
  10. 2026SemBench: A Universal Semantic Framework for LLM Evaluationcited by 0