23 citations · 25 across the 6 of their papers we have counts for
7 papers
Exploiting Large-scale Teacher-Student Training for On-device Acoustic Models
Jing Liu, Rupak Vignesh Swaminathan, Sree Hari Krishnan Parthasarathi +3
We present results from Alexa speech teams on semi-supervised learning (SSL) of acoustic models (AM) with experiments spanning over 3000 hours of GPU time, making our study one of…
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing
Chunchuan Lyu, Shay B. Cohen, Ivan Titov
Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs t…
Nonparametric Learning of Two-Layer ReLU Residual Units
Zhunxuan Wang, Linyun He, Chunchuan Lyu +1
We describe an algorithm that learns two-layer residual units using rectified linear unit (ReLU) activation: suppose the input is from a distribution with support spac…
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
Xinchi Chen, Chunchuan Lyu, Ivan Titov
Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on power…
Semantic Role Labeling with Iterative Structure Refinement
Chunchuan Lyu, Shay B. Cohen, Ivan Titov
Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions betwe…
AMR Parsing as Graph Prediction with Latent Alignment
Chunchuan Lyu, Ivan Titov
Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing…
A Unified Gradient Regularization Family for Adversarial Examples
Chunchuan Lyu, Kaizhu Huang, Hai-Ning Liang
Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data…