no code implementations • 11 Jun 2021 • Jing Liu, Rupak Vignesh Swaminathan, Sree Hari Krishnan Parthasarathi, Chunchuan Lyu, Athanasios Mouchtaris, Siegfried Kunzmann
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 the largest of its kind.
no code implementations • EMNLP 2021 • Chunchuan Lyu, Shay B. Cohen, Ivan Titov
In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training.
Ranked #22 on AMR Parsing on LDC2017T10
1 code implementation • 17 Aug 2020 • Zhunxuan Wang, Linyun He, Chunchuan Lyu, Shay B. Cohen
We describe an algorithm that learns two-layer residual units using rectified linear unit (ReLU) activation: suppose the input $\mathbf{x}$ is from a distribution with support space $\mathbb{R}^d$ and the ground-truth generative model is a residual unit of this type, given by $\mathbf{y} = \boldsymbol{B}^\ast\left[\left(\boldsymbol{A}^\ast\mathbf{x}\right)^+ + \mathbf{x}\right]$, where ground-truth network parameters $\boldsymbol{A}^\ast \in \mathbb{R}^{d\times d}$ represent a full-rank matrix with nonnegative entries and $\boldsymbol{B}^\ast \in \mathbb{R}^{m\times d}$ is full-rank with $m \geq d$ and for $\boldsymbol{c} \in \mathbb{R}^d$, $[\boldsymbol{c}^{+}]_i = \max\{0, c_i\}$.
1 code implementation • IJCNLP 2019 • Xinchi Chen, Chunchuan Lyu, Ivan Titov
In every network layer, the capsules interact with each other and with representations of words in the sentence.
1 code implementation • IJCNLP 2019 • 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 between individual argument labeling decisions.
2 code implementations • ACL 2018 • Chunchuan Lyu, Ivan Titov
AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences.
Ranked #1 on AMR Parsing on LDC2015E86
no code implementations • 19 Nov 2015 • Chunchuan Lyu, Kai-Zhu Huang, Hai-Ning Liang
In this paper, we propose a unified framework to build robust machine learning models against adversarial examples.