24 citations · 28 across the 5 of their papers we have counts for
5 papers
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation
Minghao Wu, Yufei Wang, George Foster +2
Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart. However, due to…
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
Jiaming Luo, Colin Cherry, George Foster
We conduct a large-scale fine-grained comparative analysis of machine translations (MT) against human translations (HT) through the lens of morphosyntactic divergence. Across three…
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…