Consistency Training by Synthetic Question Generation for Conversational
  Question Answering
Hamed Hematian Hemati Hamid Beigy
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 a model that
utilizes both real and augmented historical data to implicitly make the
reasoning robust to irrelevant history. To the best of our knowledge, this is
the first instance of research using question generation as a form of data
augmentation to model conversational QA settings. By citing a common modeling
error prevalent in previous research, we introduce a new baseline model and
compare our model's performance against it, demonstrating an improvement in
results, particularly when dealing with questions that include a substantial
amount of historical context. The source code can be found on our GitHub page.