TY - GEN
T1 - Crowdsourcing for evaluating machine translation quality
AU - Goto, Shinsuke
AU - Lin, Donghui
AU - Ishida, Toru
N1 - Funding Information:
This research was partially supported by a Grant-in-Aid for Scientific Research(S) (24220002, 2012 2016) from Japan Society for the Promotion of Science (JSPS), and Service Science, Solutions and Foundation Integrated Research Program from JST RISTEX.
PY - 2014
Y1 - 2014
N2 - The recent popularity of machine translation has increased the demand for the evaluation of translations. However, the traditional evaluation approach, manual checking by a bilingual professional, is too expensive and too slow. In this study, we confirm the feasibility of crowdsourcing by analyzing the accuracy of crowdsourcing translation evaluations. We compare crowdsourcing scores to professional scores with regard to three metrics: translation-score, sentence-score, and system-score. A Chinese to English translation evaluation task was designed using around the NTCIR-9 PATENT parallel corpus with the goal being 5-range evaluations of adequacy and fluency. The experiment shows that the average score of crowdsource workers well matches professional evaluation results. The system-score comparison strongly indicates that crowdsourcing can be used to find the best translation system given the input of 10 source sentence.
AB - The recent popularity of machine translation has increased the demand for the evaluation of translations. However, the traditional evaluation approach, manual checking by a bilingual professional, is too expensive and too slow. In this study, we confirm the feasibility of crowdsourcing by analyzing the accuracy of crowdsourcing translation evaluations. We compare crowdsourcing scores to professional scores with regard to three metrics: translation-score, sentence-score, and system-score. A Chinese to English translation evaluation task was designed using around the NTCIR-9 PATENT parallel corpus with the goal being 5-range evaluations of adequacy and fluency. The experiment shows that the average score of crowdsource workers well matches professional evaluation results. The system-score comparison strongly indicates that crowdsourcing can be used to find the best translation system given the input of 10 source sentence.
KW - Crowdsourcing
KW - Evaluation
KW - Machine translation
UR - http://www.scopus.com/inward/record.url?scp=85026825744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026825744&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85026825744
T3 - Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
SP - 3456
EP - 3463
BT - Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Declerck, Thierry
A2 - Mariani, Joseph
A2 - Maegaard, Bente
A2 - Moreno, Asuncion
A2 - Odijk, Jan
A2 - Mazo, Helene
A2 - Piperidis, Stelios
A2 - Loftsson, Hrafn
PB - European Language Resources Association (ELRA)
T2 - 9th International Conference on Language Resources and Evaluation, LREC 2014
Y2 - 26 May 2014 through 31 May 2014
ER -