33 citations · 60 across the 16 of their papers we have counts for
16 papers
Knowledge Graph Reasoning with Self-supervised Reinforcement Learning
Ying Ma, Owen Burns, Mingqiu Wang +6
Reinforcement learning (RL) is an effective method of finding reasoning pathways in incomplete knowledge graphs (KGs). To overcome the challenges of a large action space, a self-su…
Retrieval Augmented End-to-End Spoken Dialog Models
Mingqiu Wang, Izhak Shafran, Hagen Soltau +4
We recently developed SLM, a joint speech and language model, which fuses a pretrained foundational speech model and a large language model (LLM), while preserving the in-context l…
SLM: Bridge the thin gap between speech and text foundation models
Mingqiu Wang, Wei Han, Izhak Shafran +15
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM…
Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for Speech Understanding
Mingqiu Wang, Izhak Shafran, Hagen Soltau +4
Large Language Models (LLMs) have been applied in the speech domain, often incurring a performance drop due to misaligned between speech and language representations. To bridge thi…
MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez +4
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasi…
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…
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…
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…
RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences
Hagen Soltau, Izhak Shafran, Mingqiu Wang +1
Popular solutions to Named Entity Recognition (NER) include conditional random fields, sequence-to-sequence models, or utilizing the question-answering framework. However, they are…
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…
Word-level confidence estimation for RNN transducers
Mingqiu Wang, Hagen Soltau, Laurent El Shafey +1
Confidence estimate is an often requested feature in applications such as medical transcription where errors can impact patient care and the confidence estimate could be used to al…
R2D2: Relational Text Decoding with Transformers
Aryan Arbabi, Mingqiu Wang, Laurent El Shafey +2
We propose a novel framework for modeling the interaction between graphical structures and the natural language text associated with their nodes and edges. Existing approaches typi…
Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction
Hagen Soltau, Mingqiu Wang, Izhak Shafran +1
In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transfor…
The Medical Scribe: Corpus Development and Model Performance Analyses
Izhak Shafran, Nan Du, Linh Tran +11
There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of prov…
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Nan Du, Mingqiu Wang, Linh Tran +2
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the chal…