Machine Translationness: Machine-likeness in Machine Translation Evaluation

Joaquim Moré, Salvador Climent


Abstract
Machine translationness (MTness) is the linguistic phenomena that make machine translations distinguishable from human translations. This paper intends to present MTness as a research object and suggests an MT evaluation method based on determining whether the translation is machine-like instead of determining its human-likeness as in evaluation current approaches. The method rates the MTness of a translation with a metric, the MTS (Machine Translationness Score). The MTS calculation is in accordance with the results of an experimental study on machine translation perception by common people. MTS proved to correlate well with human ratings on translation quality. Besides, our approach allows the performance of cheap evaluations since expensive resources (e.g. reference translations, training corpora) are not needed. The paper points out the challenge of dealing with MTness as an everyday phenomenon caused by the massive use of MT.
Anthology ID:
L14-1419
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
54–61
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/506_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Joaquim Moré and Salvador Climent. 2014. Machine Translationness: Machine-likeness in Machine Translation Evaluation. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 54–61, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Machine Translationness: Machine-likeness in Machine Translation Evaluation (Moré & Climent, LREC 2014)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/506_Paper.pdf