A Challenge Set Approach to Evaluating Machine Translation

Pierre Isabelle, Colin Cherry, George Foster

[How to correct problems with metadata yourself]

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.
Anthology ID:
D17-1263
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2486–2496
Language:
URL:
https://aclanthology.org/D17-1263
DOI:
10.18653/v1/D17-1263
Bibkey:
Cite (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.
Cite (Informal):
A Challenge Set Approach to Evaluating Machine Translation (Isabelle et al., EMNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D17-1263.pdf
Attachment:
 D17-1263.Attachment.zip
Data
WMT 2016
PDF Search Attachment