CO3: Low-resource Contrastive Co-training for Generative Conversational
  Query Rewrite
Yifei Yuan Chen Shi Runze Wang Liyi Chen Renjun Hu Zengming Zhang Feijun Jiang Wai Lam
Generative query rewrite generates reconstructed query rewrites using the
conversation history while rely heavily on gold rewrite pairs that are
expensive to obtain. Recently, few-shot learning is gaining increasing
popularity for this task, whereas these methods are sensitive to the inherent
noise due to limited data size. Besides, both attempts face performance
degradation when there exists language style shift between training and testing
cases. To this end, we study low-resource generative conversational query
rewrite that is robust to both noise and language style shift. The core idea is
to utilize massive unlabeled data to make further improvements via a
contrastive co-training paradigm. Specifically, we co-train two dual models
(namely Rewriter and Simplifier) such that each of them provides extra guidance
through pseudo-labeling for enhancing the other in an iterative manner. We also
leverage contrastive learning with data augmentation, which enables our model
pay more attention on the truly valuable information than the noise. Extensive
experiments demonstrate the superiority of our model under both few-shot and
zero-shot scenarios. We also verify the better generalization ability of our
model when encountering language style shift.