293 citations · 839 across the 16 of their papers we have counts for
17 papers · 1 filter
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability
Eleftheria Briakou, Colin Cherry, George Foster
Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation e…
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Minghao Wu, George Foster, Lizhen Qu +1
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.…
The unreasonable effectiveness of few-shot learning for machine translation
Xavier Garcia, Yamini Bansal, Colin Cherry +5
We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs. We show that with only 5 examples…
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…
Toward More Effective Human Evaluation for Machine Translation
Belén Saldías, George Foster, Markus Freitag +1
Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal.…
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Markus Freitag, George Foster, David Grangier +3
Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erron…
Assessing Reference-Free Peer Evaluation for Machine Translation
Sweta Agrawal, George Foster, Markus Freitag +1
Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been…
Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag, George Foster, David Grangier +1
Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by Freitag et al. When used in place of or…
Inference Strategies for Machine Translation with Conditional Masking
Julia Kreutzer, George Foster, Colin Cherry
Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation. Given…
Re-translation versus Streaming for Simultaneous Translation
Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey +1
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes av…
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation
Naveen Arivazhagan, Colin Cherry, Te I +3
We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a liv…
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
Naveen Arivazhagan, Ankur Bapna, Orhan Firat +10
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal…
Thinking Slow about Latency Evaluation for Simultaneous Machine Translation
Colin Cherry, George Foster
Simultaneous machine translation attempts to translate a source sentence before it is finished being spoken, with applications to translation of spoken language for live streaming…
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
Gaurav Kumar, George Foster, Colin Cherry +1
We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level fea…
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry, George Foster, Ankur Bapna +2
Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by elimina…
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Mia Xu Chen, Orhan Firat, Ankur Bapna +9
The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed…
A Challenge Set Approach to Evaluating Machine Translation
Pierre Isabelle, Colin Cherry, George Foster
Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these question…