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Found 4 papers, 3 papers with code
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions.
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs
We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations.
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.
CROWN: Conversational Passage Ranking by Reasoning over Word Networks
Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers.
