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    <abstract>The dramatic improvements shown by statistical machine translation systems in recent years clearly demonstrate the benefits of having large quantities of manually translated parallel text for system training and development. And while many competing evaluation metrics exist to evaluate MT technology, most of those methods also crucially rely on the existence of one or more high quality human translations to benchmark system performance. Given the importance of human translations in this framework, understanding the particular challenges of human translation-for-MT is key, as is comprehending the relative strengths and weaknesses of human versus machine translators in the context of an MT evaluation. Vanni (2000) argued that the metric used for evaluation of competence in human language learners may be applicable to MT evaluation; we apply similar thinking to improve the prediction of MT performance, which is currently unreliable. In the current paper we explore an alternate model based upon a set of genre-defining features that prove to be consistently challenging for both humans and MT systems.</abstract>
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%0 Conference Proceedings
%T Identifying Common Challenges for Human and Machine Translation: A Case Study from the GALE Program
%A Friedman, Lauren
%A Strassel, Stephanie
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F friedman-strassel-2008-identifying
%X The dramatic improvements shown by statistical machine translation systems in recent years clearly demonstrate the benefits of having large quantities of manually translated parallel text for system training and development. And while many competing evaluation metrics exist to evaluate MT technology, most of those methods also crucially rely on the existence of one or more high quality human translations to benchmark system performance. Given the importance of human translations in this framework, understanding the particular challenges of human translation-for-MT is key, as is comprehending the relative strengths and weaknesses of human versus machine translators in the context of an MT evaluation. Vanni (2000) argued that the metric used for evaluation of competence in human language learners may be applicable to MT evaluation; we apply similar thinking to improve the prediction of MT performance, which is currently unreliable. In the current paper we explore an alternate model based upon a set of genre-defining features that prove to be consistently challenging for both humans and MT systems.
%U https://aclanthology.org/2008.amta-govandcom.10
%P 364-369

Markdown (Informal)

[Identifying Common Challenges for Human and Machine Translation: A Case Study from the GALE Program](https://aclanthology.org/2008.amta-govandcom.10) (Friedman & Strassel, AMTA 2008)

• Identifying Common Challenges for Human and Machine Translation: A Case Study from the GALE Program (Friedman & Strassel, AMTA 2008)

ACL

• Lauren Friedman and Stephanie Strassel. 2008. Identifying Common Challenges for Human and Machine Translation: A Case Study from the GALE Program. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT, pages 364–369, Waikiki, USA. Association for Machine Translation in the Americas.