机器中的齿轮，各司其职——AMI 提交至 WMT24 通用翻译任务的参赛系统
Cogs in a Machine, Doing What They're Meant to Do -- The AMI Submission to the WMT24 General Translation Task
AI总结：
本文介绍Árni Magnusson研究所提交至WMT24通用翻译任务的英冰翻译系统，通过多模型架构、数据筛选及LLM生成合成数据，显著提升了翻译质量。
摘要：
本文介绍了Árni Magnusson研究所团队提交至WMT24通用翻译任务的参赛成果，研究方向为英语到冰岛语的翻译。我们的系统包含4个翻译模型和1个语法纠错模型。为训练模型，我们精心整理数据集，主动过滤掉可能对系统输出质量产生不利影响的句对。所用数据部分来自人工翻译，部分为合成生成，其中一部分合成数据由大语言模型（LLM）生成，我们发现这类数据可显著提升系统的翻译能力。
英文摘要：
This paper presents the submission of the Árni Magnusson Institute's team to the WMT24 General translation task. We work on the English->Icelandic translation direction. Our system comprises four translation models and a grammar correction model. For training our models we carefully curate our datasets, aggressively filtering out sentence pairs that may detrimentally affect the quality of our system's output. Some of our data are collected from human translations and some are synthetically generated. A part of the synthetic data is generated using an LLM, and we find that it increases the translation capability of our system significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
