  Conversational Query Reformulation (CQR) has significantly advanced in
addressing the challenges of conversational search, particularly those stemming
from the latent user intent and the need for historical context. Recent works
aimed to boost the performance of CRQ through alignment. However, they are
designed for one specific retrieval system, which potentially results in poor
generalization. To overcome this limitation, we present a novel framework
AdaCQR. By aligning reformulation models with both term-based and
semantic-based retrieval systems, AdaCQR enhances the generalizability of
information-seeking queries across diverse retrieval environments through a
dual-phase training strategy. We also developed two effective approaches for
acquiring superior labels and diverse input candidates, boosting the efficiency
and robustness of the framework. Experimental evaluations on the TopiOCQA and
QReCC datasets demonstrate that AdaCQR significantly outperforms existing
methods, offering both quantitative and qualitative improvements in
conversational query reformulation.
