177 citations · 392 across the 27 of their papers we have counts for
8 papers · 1 filter
Can Multilinguality benefit Non-autoregressive Machine Translation?
Sweta Agrawal, Julia Kreutzer, Colin Cherry
Non-autoregressive (NAR) machine translation has recently achieved significant improvements, and now outperforms autoregressive (AR) models on some benchmarks, providing an efficie…
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 Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
Orevaoghene Ahia, Julia Kreutzer, Sara Hooker
A "bigger is better" explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-rest…
Evaluating Multiway Multilingual NMT in the Turkic Languages
Jamshidbek Mirzakhalov, Anoop Babu, Aigiz Kunafin +11
Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-…
Modelling Latent Translations for Cross-Lingual Transfer
Edoardo Maria Ponti, Julia Kreutzer, Ivan Vulić +1
While achieving state-of-the-art results in multiple tasks and languages, translation-based cross-lingual transfer is often overlooked in favour of massively multilingual pre-train…
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
Samuel Kiegeland, Julia Kreutzer
Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multip…