33 citations · 113 across the 13 of their papers we have counts for
25 papers
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…
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions
Cuong Hoang, Devendra Sachan, Prashant Mathur +2
Several recent studies have reported dramatic performance improvements in neural machine translation (NMT) by augmenting translation at inference time with fuzzy-matches retrieved…
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
Cuong Hoang, Devendra Sachan, Prashant Mathur +2
We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the id…
Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric
Giorgos Vernikos, Brian Thompson, Prashant Mathur +1
We hypothesize that existing sentence-level machine translation (MT) metrics become less effective when the human reference contains ambiguities. To verify this hypothesis, we pres…
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…
Prosodic Alignment for off-screen automatic dubbing
Yogesh Virkar, Marcello Federico, Robert Enyedi +1
The goal of automatic dubbing is to perform speech-to-speech translation while achieving audiovisual coherence. This entails isochrony, i.e., translating the original speech by als…