mlsanityData to 7 Oct 2026

Towards Better Question Generation in QA-based Event Extraction

arXiv 2405.10517 ↗First posted 17 May 2024cs.CLcited by 6

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Abstract (arXiv)

Event Extraction (EE) is an essential information extraction task that aims to extract event-related information from unstructured texts. The paradigm of this task has shifted from conventional classification-based methods to more contemporary question-answering-based (QA-based) approaches. However, in QA-based EE, the quality of the questions dramatically affects the extraction accuracy, and how to generate high-quality questions for QA-based EE remains a challenge. In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a reinforcement learning method, RLQG, for QA-based EE that can generate generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. The extensive experiments conducted on ACE and RAMS datasets have strongly validated our approach's effectiveness, which also demonstrates its robustness in scenarios with limited training data. The corresponding code of RLQG is released for further research.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 48 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

32 of 48 references.

References · 32

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  8. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  9. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  10. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  11. 2017Deep reinforcement learning from human preferencescited by 3,846
  12. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
20 more
  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  2. 2023Qwen Technical Reportcited by 3,273
  3. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  4. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  5. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  6. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  7. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  8. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  9. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  10. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  11. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 261
  12. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  13. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  14. 2019Multi-Sentence Argument Linkingcited by 80
  15. 2022Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extractioncited by 54
  16. 2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
  17. 2021Controllable Generation from Pre-trained Language Models via Inverse Promptingcited by 29
  18. 2023Event Extraction as Question Generation and Answeringcited by 15
  19. 2022EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extractioncited by 11
  20. 2023An AMR-based Link Prediction Approach for Document-level Event Argument Extractioncited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  2. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  3. 2025Event Extraction in Large Language Modelcited by 5
  4. 2025Macro Graph of Experts for Billion-Scale Multi-Task Recommendationcited by 2
  5. 2026Improving Information Extraction with Learned Queriescited by 0
  6. 2026EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewardscited by 0