Towards More Realistic Generation of Information-Seeking Conversations

25 May 2022  ·  Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang ·

In this paper, we introduce a novel framework SimSeek (simulating information-seeking conversation from unlabeled documents) and compare two variants of it to provide a deeper perspective into the information-seeking behavior. We first introduce a strong simulator for information-symmetric conversation, SimSeek-sym, where questioner and answerer share all knowledge when conversing with one another. Although it simulates reasonable conversations, we take a further step toward more realistic information-seeking conversation. Hence, we propose SimSeek-asym that assumes information asymmetry between two agents, which encourages the questioner to seek new information from an inaccessible document. In our experiments, we demonstrate that SimSeek-asym successfully generates information-seeking conversations for two downstream tasks, CQA and conversational search. In particular, SimSeek-asym improves baseline models by 1.1-1.9 F1 score in QuAC, and by 1.1 of MRR in OR-QuAC. Moreover, we thoroughly analyze our synthetic datasets to identify crucial factors for realistic information-seeking conversation.

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