Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing question reformulation models are based on supervised question labels without considering feedback information from answers. |
| Approach: | They propose a question reformulation model that integrates conversational history information with reinforcement learning. |
| Outcome: | The proposed model is more effective in conversational machine comprehension with reinforcement learning. |
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| Challenge: | Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited. |
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| Challenge: | Recent research has focused on synthetically generating a question from a given context and an annotated answer by training an additional generative model. |
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| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
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Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
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| Challenge: | Existing data augmentation techniques for natural language processing tasks are difficult to design. |
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| Challenge: | Existing methods for conversational query reformulation depend on human annotations. |
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Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Unlike static ‘rewrite, retrieve, and generate’ pipelines, ChatR1 interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through RL. |
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| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
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