49 citations · 85 across the 11 of their papers we have counts for
8 papers · 1 filter
Learning Neural Models for Natural Language Processing in the Face of Distributional Shift
Paul Michel
The dominating NLP paradigm of training a strong neural predictor to perform one task on a specific dataset has led to state-of-the-art performance in a variety of applications (eg…
Findings of the First Shared Task on Machine Translation Robustness
Xian Li, Paul Michel, Antonios Anastasopoulos +7
We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in…
Are Sixteen Heads Really Better than One?
Paul Michel, Omer Levy, Graham Neubig
Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predic…
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
Paul Michel, Xian Li, Graham Neubig +1
Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of seq…
compare-mt: A Tool for Holistic Comparison of Language Generation Systems
Graham Neubig, Zi-Yi Dou, Junjie Hu +4
In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goa…
MTNT: A Testbed for Machine Translation of Noisy Text
Paul Michel, Graham Neubig
Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating nois…