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        <title>A Challenge Set Approach to Evaluating Machine Translation</title>
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        <namePart type="given">Pierre</namePart>
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            <namePart type="given">Martha</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system’s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.</abstract>
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%0 Conference Proceedings
%T A Challenge Set Approach to Evaluating Machine Translation
%A Isabelle, Pierre
%A Cherry, Colin
%A Foster, George
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F isabelle-etal-2017-challenge
%X Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system’s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.
%R 10.18653/v1/D17-1263
%U https://aclanthology.org/D17-1263
%U https://doi.org/10.18653/v1/D17-1263
%P 2486-2496

Markdown (Informal)

[A Challenge Set Approach to Evaluating Machine Translation](https://aclanthology.org/D17-1263) (Isabelle et al., EMNLP 2017)

• A Challenge Set Approach to Evaluating Machine Translation (Isabelle et al., EMNLP 2017)

ACL

• Pierre Isabelle, Colin Cherry, and George Foster. 2017. A Challenge Set Approach to Evaluating Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2486–2496, Copenhagen, Denmark. Association for Computational Linguistics.