5.7k citations · 8.2k across the 48 of their papers we have counts for
12 papers · 1 filter
Speech Aware Dialog System Technology Challenge (DSTC11)
Hagen Soltau, Izhak Shafran, Mingqiu Wang +6
Most research on task oriented dialog modeling is based on written text input. However, users interact with practical dialog systems often using speech as input. Typically, systems…
AnyTOD: A Programmable Task-Oriented Dialog System
Jeffrey Zhao, Yuan Cao, Raghav Gupta +6
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training. We view TOD as a program executed by…
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners
Shen Yan, Tao Zhu, Zirui Wang +5
We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model…
Knowledge-grounded Dialog State Tracking
Dian Yu, Mingqiu Wang, Yuan Cao +3
Knowledge (including structured knowledge such as schema and ontology, and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for uns…
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao, Jeffrey Zhao, Dian Yu +4
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (…
Unsupervised Slot Schema Induction for Task-oriented Dialog
Dian Yu, Mingqiu Wang, Yuan Cao +3
Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manual…
The Implicit Length Bias of Label Smoothing on Beam Search Decoding
Bowen Liang, Pidong Wang, Yuan Cao
Label smoothing is ubiquitously applied in Neural Machine Translation (NMT) training. While label smoothing offers a desired regularization effect during model training, in this pa…
Building Machine Translation Systems for the Next Thousand Languages
Ankur Bapna, Isaac Caswell, Julia Kreutzer +21
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results i…
Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Raghav Gupta, Harrison Lee, Jeffrey Zhao +3
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge. Recent works have leveraged…
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation
Yong Cheng, Ankur Bapna, Orhan Firat +3
Multilingual neural machine translation models are trained to maximize the likelihood of a mix of examples drawn from multiple language pairs. The dominant inductive bias applied t…
Description-Driven Task-Oriented Dialog Modeling
Jeffrey Zhao, Raghav Gupta, Yuan Cao +6
Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks. Such information is conventionally specified in…
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
Aditya Siddhant, Ankur Bapna, Orhan Firat +4
Achieving universal translation between all human language pairs is the holy-grail of machine translation (MT) research. While recent progress in massively multilingual MT is one s…