29 research outputs found
Findings of the 2019 Conference on Machine Translation (WMT19)
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 translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation
Augmenting Large Language Model Translators via Translation Memories
Using translation memories (TMs) as prompts is a promising approach to
in-context learning of machine translation models. In this work, we take a step
towards prompting large language models (LLMs) with TMs and making them better
translators. We find that the ability of LLMs to ``understand'' prompts is
indeed helpful for making better use of TMs. Experiments show that the results
of a pre-trained LLM translator can be greatly improved by using high-quality
TM-based prompts. These results are even comparable to those of the
state-of-the-art NMT systems which have access to large-scale in-domain
bilingual data and are well tuned on the downstream tasks.Comment: Accepted to Findings of ACL 202
Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task
This paper introduces WeChat's participation in WMT 2022 shared biomedical
translation task on Chinese to English. Our systems are based on the
Transformer, and use several different Transformer structures to improve the
quality of translation. In our experiments, we employ data filtering, data
generation, several variants of Transformer, fine-tuning and model ensemble.
Our ChineseEnglish system, named Summer, achieves the highest BLEU score
among all submissions
Findings of the WMT 2020 shared task on quality estimation
© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website; https://www.aclweb.org/anthology/2020.wmt-1.79We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels. This edition included new data with open domain texts, direct assessment annotations, and multiple language pairs: English-German, English-Chinese, Russian-English, Romanian-English, Estonian-English, Sinhala-English and Nepali-English data for the sentence-level subtasks, English-German and English-Chinese for the word-level subtask, and English-French data for the document-level subtask. In addition, we made neural machine translation models available to participants. 19 participating teams from 27 institutions submitted altogether 1374 systems to different task variants and language pairs