Consistency Training by Synthetic Question Generation for Conversational Question Answering

arXiv:2404.11109v1 Announce Type: new
Abstract: Efficiently modeling historical information is a critical component in addressing user queries within a conversational question-answering (QA) context, as historical context plays a vital role in clarifying the user's questions. However, irrelevant history induces noise in the reasoning process, especially for those questions with a considerable historical context. In our novel model-agnostic approach, referred to as CoTaH (Consistency-Trained augmented History), we augment the historical information with synthetic questions and subsequently employ consistency training to train …

abstract arxiv context conversational cs.cl history however information modeling noise process queries question question answering questions reasoning role synthetic training type vital