- Towards Better Question Generation in QA-based Event Extraction
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
- Towards Better Question Generation in QA-based Event Extraction
In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a rein- forcement learning method, RLQG, for QA- based EE that can generate generalizable, high- quality, and context-dependent questions and provides clear guidance to QA models
- RLQG - Towards Better QG in QA-based EE - GitHub
[2024 05] Our paper is accepted as a findings paper in ACL2024! We propose a novel framework RLQG for generating better questions in QA-based event extraction via reinforcement learning, the paper is available here
- Towards Better Question Generation in QA-Based Event Extraction
This work introduces a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner and outperforms prior methods substantially
- Towards Better Question Generation in QA-based Event Extraction
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
- Towards Better Question Generation in QA-based Event Extraction
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
- Towards Better Question Generation in QA-based Event Extraction
In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a rein-forcement learning method, RLQG, for QA-based EE that can generate generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models
- Towards Better Question Generation in QA-Based Event Extraction
In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a reinforcement learning method for QA-Based EE that can generate fluent, generalizable, and context-dependent questions and provides clear guidance to QA models
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