187 citations · 269 across the 5 of their papers we have counts for
6 papers
Prompting PaLM for Translation: Assessing Strategies and Performance
David Vilar, Markus Freitag, Colin Cherry +3
Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an i…
Scaling Up Influence Functions
Andrea Schioppa, Polina Zablotskaia, David Vilar +1
We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian…
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
Julia Kreutzer, David Vilar, Artem Sokolov
Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or di…
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…
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
Matt Post, David Vilar
The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however…
Sockeye: A Toolkit for Neural Machine Translation
Felix Hieber, Tobias Domhan, Michael Denkowski +4
We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and apply…