69 citations · 99 across the 7 of their papers we have counts for
7 papers
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
Yuqiao Wen, Behzad Shayegh, Chenyang Huang +2
The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alt…
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization
Puyuan Liu, Chenyang Huang, Lili Mou
Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not requ…
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
Chenyang Huang, Hao Zhou, Osmar R. Zaïane +2
How do we perform efficient inference while retaining high translation quality? Existing neural machine translation models, such as Transformer, achieve high performance, but they…
Simulated Annealing for Emotional Dialogue Systems
Chengzhang Dong, Chenyang Huang, Osmar Zaïane +1
Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions. In this study, we consider the task of expressing a…
A Globally Normalized Neural Model for Semantic Parsing
Chenyang Huang, Wei Yang, Yanshuai Cao +2
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued s…
Boundary-Aware Segmentation Network for Mobile and Web Applications
Xuebin Qin, Deng-Ping Fan, Chenyang Huang +6
Although deep models have greatly improved the accuracy and robustness of image segmentation, obtaining segmentation results with highly accurate boundaries and fine structures is…
Optimizing Deeper Transformers on Small Datasets
Peng Xu, Dhruv Kumar, Wei Yang +6
It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layer…