46 citations · 91 across the 12 of their papers we have counts for
10 papers · 1 filter
Hierarchical Sketch Induction for Paraphrase Generation
Tom Hosking, Hao Tang, Mirella Lapata
We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch. We introduce Hierarchical Refinement Qu…
On the Difficulty of Segmenting Words with Attention
Ramon Sanabria, Hao Tang, Sharon Goldwater
Word segmentation, the problem of finding word boundaries in speech, is of interest for a range of tasks. Previous papers have suggested that for sequence-to-sequence models traine…
An Unsupervised Autoregressive Model for Speech Representation Learning
Yu-An Chung, Wei-Ning Hsu, Hao Tang +1
This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that a…
On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition
Hao Tang, James Glass
Recurrent neural networks have been the dominant models for many speech and language processing tasks. However, we understand little about the behavior and the class of functions r…
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition
Wei-Ning Hsu, Hao Tang, James Glass
The current trend in automatic speech recognition is to leverage large amounts of labeled data to train supervised neural network models. Unfortunately, obtaining data for a wide r…
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition
Hao Tang, Wei-Ning Hsu, Francois Grondin +1
Speech recognizers trained on close-talking speech do not generalize to distant speech and the word error rate degradation can be as large as 40% absolute. Most studies focus on ta…