31 citations · 62 across the 7 of their papers we have counts for
12 papers
Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
Weiting Tan, Shuoyang Ding, Huda Khayrallah +1
Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserv…
The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task
Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post +2
This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared tas…
Levenshtein Training for Word-level Quality Estimation
Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post +1
We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained…
Evaluating Saliency Methods for Neural Language Models
Shuoyang Ding, Philipp Koehn
Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same predicti…
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
Yiming Wang, Tongfei Chen, Hainan Xu +7
We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neu…
Saliency-driven Word Alignment Interpretation for Neural Machine Translation
Shuoyang Ding, Hainan Xu, Philipp Koehn
Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpreta…