134 citations · 414 across the 17 of their papers we have counts for
17 papers
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Benjamin Hsu, Xiaoyu Liu, Huayang Li +6
Document translation poses a challenge for Neural Machine Translation (NMT) systems. Most document-level NMT systems rely on meticulously curated sentence-level parallel data, assu…
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Gabriele Sarti, Phu Mon Htut, Xing Niu +4
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation o…
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…
Sockeye 3: Fast Neural Machine Translation with PyTorch
Felix Hieber, Michael Denkowski, Tobias Domhan +11
Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced…
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities
Benjamin Hsu, Graham Horwood
Identifying related entities and events within and across documents is fundamental to natural language understanding. We present an approach to entity and event coreference resolut…
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…