57 citations · 82 across the 8 of their papers we have counts for
11 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…
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
Suvodeep Majumder, Stanislas Lauly, Maria Nadejde +2
This paper addresses the task of contextual translation using multi-segment models. Specifically we show that increasing model capacity further pushes the limits of this approach a…
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…
Improving Gender Translation Accuracy with Filtered Self-Training
Prafulla Kumar Choubey, Anna Currey, Prashant Mathur +1
Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context. Furthermore, these incorrectly gendered…
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…