You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

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 #18 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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.