Magdalena Kaiser
ABSTRACT
This PhD thesis will explore conversational question answering with a special emphasis on incorporating user feedback. As preliminary work, we developed a conversational passage retrieval system in the scope of the TREC Conversational Assistance Track 2019. Our current focus is to develop methods based on reinforcement learning to incorporate implicit user feedback in form of question reformulations for conversational QA over knowledge graphs. Finally, we plan to design a conversational QA system operating on heterogeneous sources.
- Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. In ICLR.Google Scholar
- Magdalena Kaiser, Rishiraj Saha Roy, and Gerhard Weikum. 2020. Conversational Question Answering over Passages by Leveraging Word Proximity Networks. In SIGIR.Google Scholar
- Bernhard Kratzwald and Stefan Feuerriegel. 2019. Learning from on-line user feedback in neural question answering on the web. In WWW.Google Scholar
Index Terms
- Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources
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