  This paper presents an empirical study of conversational question
reformulation (CQR) with sequence-to-sequence architectures and pretrained
language models (PLMs). We leverage PLMs to address the strong token-to-token
independence assumption made in the common objective, maximum likelihood
estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue
systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset
as an in-domain task and validate the models using data from the TREC 2019 CAsT
Track as an out-domain task. Examining a variety of architectures with
different numbers of parameters, we demonstrate that the recent text-to-text
transfer transformer (T5) achieves the best results both on CANARD and CAsT
with fewer parameters, compared to similar transformer architectures.
