Pretrained language models have significantly improved the performance of down-stream tasks, for example extractive question answering, by providing high-quality contextualized word embeddings. However, learning question answering models still need large-scale data annotation in specific domains. In this work, we propose a cooperative, self-play learning model for question generation and answering. We implemented a masked answer entity extraction task with an interactive learning environment , containing a question generator and a question extractor . Given a passage with a mask, a question generator asks a question about the masked entity, meanwhile the extractor is trained to extract the masked entity with the generated question and raw texts. With this strategy, we can train question generation and answering models on any textual corpora without annotation. To further improve the performances of the question answering model, we propose a reinforcement learning method that rewards generated questions that improves the extraction learning. Experimental results showed that our model outperforms the state-of-the-art  pretrained language models on standard question answering benchmarks, and reaches the state-of-the-art performance under the zero-shot learning setting.
Pretrained language models have significantly improved the performance of down-stream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, learning question answering models still need large-scaled data annotation in specific domains. In this work, we propose a cooperative, self-play learning framework, REGEX, for question generation and answering. REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor . Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a reinforcement learning technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX outperforms the state-of-the-art (SOTA) pretrained language models and zero-shot approaches on standard question-answering benchmarks, and yields the new SOTA performance under the zero-shot setting.
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tasks, for example
language understanding tasks, including
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large-scale
large-scaled
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model
framework, REGEX,
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We implemented
REGEX is built upon
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entity
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, containing a question generator and a question extractor
containing an answer entity REcognizer, a question Generator, and an answer EXtractor
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mask, a question generator asks a question about the
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meanwhile
the generator generates a question around the entity, and
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With this strategy, we can train
The framework allows the training of
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textual
text
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To further improve the performances of the question answering model, we propose
We further leverage
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method that rewards generated questions that improves the extraction learning. Experimental results showed that our model
technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX
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(SOTA)
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on standard question answering
and zero-shot approaches on standard question-answering
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reaches the state-of-the-art
yields the new SOTA
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learning