67 citations · 71 across the 7 of their papers we have counts for
12 papers
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey, Maria Nădejde, Raghavendra Pappagari +5
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accurac…
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
Maria Nădejde, Anna Currey, Benjamin Hsu +3
The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations ar…
Faithful Target Attribute Prediction in Neural Machine Translation
Xing Niu, Georgiana Dinu, Prashant Mathur +1
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predict…
Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
Weijia Xu, Xing Niu, Marine Carpuat
While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic grad…
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Tobias Domhan, Michael Denkowski, David Vilar +3
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of M…
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Xing Niu, Prashant Mathur, Georgiana Dinu +1
Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics…