13 citations · 21 across the 6 of their papers we have counts for
6 papers
On Instruction-Finetuning Neural Machine Translation Models
Vikas Raunak, Roman Grundkiewicz, Marcin Junczys-Dowmunt
In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) i…
PyMarian: Fast Neural Machine Translation and Evaluation in Python
Thamme Gowda, Roman Grundkiewicz, Elijah Rippeth +2
The deep learning language of choice these days is Python; measured by factors such as available libraries and technical support, it is hard to beat. At the same time, software wri…
SOTASTREAM: A Streaming Approach to Machine Translation Training
Matt Post, Thamme Gowda, Roman Grundkiewicz +3
Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparatio…
Escaping the sentence-level paradigm in machine translation
Matt Post, Marcin Junczys-Dowmunt
It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all transl…
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation
Nikolay Bogoychev, Marcin Junczys-Dowmunt, Kenneth Heafield +1
In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In or…
Predicting Target Language CCG Supertags Improves Neural Machine Translation
Maria Nadejde, Siva Reddy, Rico Sennrich +4
Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepo…