Search Results for author:
Found 19 papers, 6 papers with code
Sockeye 2: A Toolkit for Neural Machine Translation
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.
A Natural Diet: Towards Improving Naturalness of Machine Translation Output
In this work we propose a method for training MT systems to achieve a more natural style, i. e. mirroring the style of text originally written in the target language.
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
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 different levels of quality or complexity.
A Statistical Extension of Byte-Pair Encoding
Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks.
Controlling Machine Translation for Multiple Attributes with Additive Interventions
Fine-grained control of machine translation (MT) outputs along multiple attributes is critical for many modern MT applications and is a requirement for gaining users’ trust.
Prompting PaLM for Translation: Assessing Strategies and Performance
Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages.
Scaling Up Influence Functions
We address efficient calculation of influence functions for tracking predictions back to the training data.
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
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 different levels of quality or complexity.
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models
In this paper we explore the use of Learning Hidden Unit Contribution for the task of neural machine translation.
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
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.
Sockeye: A Toolkit for Neural Machine Translation
Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.
The taraX\"U corpus of human-annotated machine translations
Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows.
Involving Language Professionals in the Evaluation of Machine Translation
Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop.
