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Found 7 papers, 3 papers with code
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs.
Pseudo-Label Training and Model Inertia in Neural Machine Translation
While the effect of PLT on quality is well-documented, we highlight a lesser-known effect: PLT can enhance a model's stability to model updates and input perturbations, a set of properties we call model inertia.
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
Sockeye 3: Fast Neural Machine Translation with PyTorch
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities
Identifying related entities and events within and across documents is fundamental to natural language understanding.
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers.
