mlsanityData to 14 Oct 2026

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

arXiv 2306.06058 ↗First posted 9 Jun 2023cs.CLcited by 13

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Abstract (arXiv)

The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners. In this work, we propose a novel task of Trans-Lingual Definition Generation (TLDG), which aims to generate definitions in another language, i.e., the native speaker's language. Initially, we explore the unsupervised manner of this task and build up a simple implementation of fine-tuning the multi-lingual machine translation model. Then, we develop two novel methods, Prompt Combination and Contrastive Prompt Learning, for further enhancing the quality of the generation. Our methods are evaluated against the baseline Pipeline method in both rich- and low-resource settings, and we empirically establish its superiority in generating higher-quality trans-lingual definitions.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 9 Jun 2023.

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27 of 39 references.

References · 27

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  5. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  6. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  7. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  8. 2021GPT Understands, Toocited by 716
  9. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  10. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 593
  11. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
  12. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
15 more
  1. 2021True Few-Shot Learning with Language Modelscited by 344
  2. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  3. 2021PPT: Pre-trained Prompt Tuning for Few-shot Learningcited by 232
  4. 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
  5. 2016Definition Modeling: Learning to define word embeddings in natural languagecited by 46
  6. 2018Conditional Generators of Words Definitionscited by 35
  7. 2017Learning to Explain Non-Standard English Words and Phrasescited by 21
  8. 2021Exploring Prompt-based Few-shot Learning for Grounded Dialog Generationcited by 20
  9. 2022Multitasking Framework for Unsupervised Simple Definition Generationcited by 18
  10. 2022Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generationcited by 15
  11. 2019Incorporating Sememes into Chinese Definition Modelingcited by 13
  12. 2020VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modelingcited by 13
  13. 2022On Measuring Social Biases in Prompt-Based Multi-Task Learningcited by 10
  14. 2022Fine-grained Contrastive Learning for Definition Generationcited by 10
  15. 2022Psychology-guided Controllable Story Generationcited by 2

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Citing papers per year: 2023: 1; 2024: 6; 2025: 5; 2026: 1.

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  1. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
  2. 2025Preference Leakage: A Contamination Problem in LLM-as-a-judgecited by 125
  3. 2024Incremental Residual Concept Bottleneck Modelscited by 43
  4. 2024A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Modelscited by 5
  5. 2023On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Softwarecited by 3
  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. 2025SlangDIT: Benchmarking LLMs in Interpretative Slang Translationcited by 3
  9. 2024DORE: A Dataset For Portuguese Definition Generationcited by 2
  10. 2025Can Large Language Models Understand Internet Buzzwords Through User-Generated Contentcited by 2
  11. 2025CLIX: Cross-Lingual Explanations of Idiomatic Expressionscited by 1
  12. 2026Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionariescited by 1
1 more
  1. 2025Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Modelscited by 0