55 citations · 85 across the 6 of their papers we have counts for
6 papers
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
Sidi Lu, Wenbo Zhao, Chenyang Tao +4
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models. It is designed to avoid catastrophic forgetting while achieving gua…
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao +4
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the…
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
Michael Regan, Pushpendre Rastogi, Arpit Gupta +1
We present Contextual Query Rewrite (CQR) a dataset for multi-domain task-oriented spoken dialogue systems that is an extension of the Stanford dialog corpus (Eric et al., 2017a).…
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
Amr Sharaf, Arpit Gupta, Hancheng Ge +2
In the slot-filling paradigm, where a user can refer back to slots in the context during the conversation, the goal of the contextual understanding system is to resolve the referri…
Contextual Slot Carryover for Disparate Schemas
Chetan Naik, Arpit Gupta, Hancheng Ge +2
In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring…
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
Anjishnu Kumar, Arpit Gupta, Julian Chan +9
This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development…