Survey of Low-Resource Machine Translation

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computational Linguistics Pub Date : 2021-09-01 DOI:10.1162/coli_a_00446

B. Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindvrich Helcl, Alexandra Birch

引用次数: 70

Abstract

Abstract 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 language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.

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低资源机器翻译研究综述

摘要本文对低资源机器翻译(MT)的研究现状进行了综述。目前世界上大约有7000种语言，几乎所有的语言对都缺乏训练机器翻译模型的重要资源。在翻译训练数据非常少的情况下，如何产生有用的翻译模型的研究越来越受到关注。我们对这一主题研究领域进行了总结，并对研究人员在最近的几个低资源机器翻译共享任务中评估的技术进行了描述。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Linguistics 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Computational Linguistics, the longest-running publication dedicated solely to the computational and mathematical aspects of language and the design of natural language processing systems, provides university and industry linguists, computational linguists, AI and machine learning researchers, cognitive scientists, speech specialists, and philosophers with the latest insights into the computational aspects of language research.