Papers with questioner

3 papers
Generating Information-Seeking Conversations from Unlabeled Documents (2022.emnlp-main)

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Challenge: a novel framework for conversational question answering from unlabeled documents has been proposed . a large-scale dataset of synthetic conversations is available for use in real-world applications .
Approach: They propose a framework for conversational question answering from unlabeled documents . they propose 'SimSeek' framework that simulates conversation from unlabelled documents based on two scenarios .
Outcome: The proposed framework achieves state-of-the-art performance on a recent CQA benchmark, QuAC.
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)

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Challenge: The 20 Questions (Q20) game encourages deductive reasoning and creativity.
Approach: They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward .
Outcome: The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment.
Can You Unpack That? Learning to Rewrite Questions-in-Context (D19-1)

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Challenge: Existing QA datasets lack key NLP problems like coreference and ellipsis resolution.
Approach: They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer.
Outcome: The proposed task is based on a dataset of 40,527 questions based in QuAC . it requires models to link questions together to resolve conversational dependencies .

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