Researcher in Computer Science and Engineering
PhD in Computer Engineering from
Department of Computer Engineering, Koç University.
English Word of the Day
October 22, 2017
Enerji Harcamalarını Azaltmak için Bulut Monitorü
A Cloud Monitor for Reducing Energy Consumption
Ergun Biçici. Enerji Harcamalarını Azaltmak için Bulut Monitorü (A Cloud Monitor for Reducing Energy Consumption). In Proc. of the First Symposium on Cloud Computing and Big Data (B3S17), Antalya, Turkey, pages 117-122, 10 2017. TÜRKİYE BİLİMSEL ve TEKNOLOJİK ARAŞTIRMA KURUMU (TÜBITAK). [WWW] [bibtex-entry]
B3S'17: 1. Ulusal Bulut Bilişim ve Büyük Veri Sempozyumu (http://www.b3s.b3lab.org/)
World class organization at the top touristic place and venue about cloud computing, big data, machine learning, and related applications.
October 7, 2017
Predicting Translation Performance with Referential Translation Machines
Ergun Biçici. Predicting Translation Performance with Referential Translation Machines. In Proc. of the Second Conference on Statistical Machine Translation (WMT17), Copenhagen, Denmark, September 2017. Association for Computational Linguistics. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction.
Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3rd system results for German to English sentence-level prediction of translation quality and the 2nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character n-grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.
Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3rd system results for German to English sentence-level prediction of translation quality and the 2nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character n-grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.
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