23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
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- Georgia Institute of TechnologyUS17 papers
25 papers · 2 filters
Scalable language model adaptation for spoken dialogue systems
Ankur Gandhe, Ariya Rastrow, Bjorn Hoffmeister
Language models (LM) for interactive speech recognition systems are trained on large amounts of data and the model parameters are optimized on past user data. New application inten…
Joint Entity Extraction and Assertion Detection for Clinical Text
Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia
Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the exist…
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Aditya Siddhant, Anuj Goyal, Angeliki Metallinou
User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these un…
Multi-encoder multi-resolution framework for end-to-end speech recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end Automatic Speech Recognition (ASR). The joint…
User Modeling for Task Oriented Dialogues
Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur +1
We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspe…
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Surafel M. Lakew, Aliia Erofeeva, Matteo Negri +2
We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for…