5IDER: Unified Query Rewriting for Steering, Intent Carryover,
  Disfluencies, Entity Carryover and Repair
Jiarui Lu Bo-Hsiang Tseng Joel Ruben Antony Moniz Site Li Xueyun Zhu Hong Yu Murat Akbacak
Providing voice assistants the ability to navigate multi-turn conversations
is a challenging problem. Handling multi-turn interactions requires the system
to understand various conversational use-cases, such as steering, intent
carryover, disfluencies, entity carryover, and repair. The complexity of this
problem is compounded by the fact that these use-cases mix with each other,
often appearing simultaneously in natural language. This work proposes a
non-autoregressive query rewriting architecture that can handle not only the
five aforementioned tasks, but also complex compositions of these use-cases. We
show that our proposed model has competitive single task performance compared
to the baseline approach, and even outperforms a fine-tuned T5 model in
use-case compositions, despite being 15 times smaller in parameters and 25
times faster in latency.