CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite

TL;DR

This paper introduces CO3, a contrastive co-training approach that leverages unlabeled data to improve low-resource conversational query rewriting, making it more robust to noise and language style shifts.

Contribution

The paper proposes a novel contrastive co-training framework with dual models and data augmentation, enhancing low-resource query rewriting performance and generalization.

Findings

Outperforms existing methods in few-shot and zero-shot settings.

Demonstrates robustness to language style shifts.

Shows improved generalization with unlabeled data.

Abstract

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.