33 citations · 39 across the 9 of their papers we have counts for
11 papers · 1 filter
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…
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…