Predicting Translation Performance with Referential Translation Machines

By Ergun Biçici. 2017

8 citations recorded by the source corpus.

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 3 rd system re-sults for German to English sentence-level prediction of translation quality and the 2 nd 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.

Subfields: Artificial Intelligence, Language and Linguistics, Evaluation & Benchmarks, Machine Translation

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Citations in the corpus

Findings of the 2017 Conference on Machine Translation (WMT17) (2017, background): “, 2017) RTM Referential Translation Machines, Turkey (Biçici, 2017) SHEF University of Sheffield, UK (Blain et al.”