Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

We explore the use of natural language prompts for controlling various aspects of the
outputs generated by machine translation models. We demonstrate that natural language …

We show that Bayes' rule provides an effective mechanism for creating document translation
models that can be learned from only parallel sentences and monolingual documents a …

Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in
many natural language processing scenarios, like reranking, which is to select the best …

This document describes the findings of the Third Workshop on Neural Generation and
Translation, held in concert with the annual conference of the Empirical Methods in Natural …

We participate in the WMT 2020 shared news translation task on Chinese to English. Our
system is based on the Transformer (Vaswani et al., 2017a) with effective variants and the …

Neural machine translation (NMT) has arguably achieved human level parity when trained
and evaluated at the sentence-level. Document-level neural machine translation has …