YSDA Participation in the WMT’16 Quality Estimation Shared Task

By Anna Kozlova, Mariya Shmatova, Anton Frolov. 2016

27 citations recorded by the source corpus.

This paper describes Yandex School of Data Analysis (YSDA) submission for WMT2016 Shared Task on Quality Estimation (QE) / Task 1: Sentence-level prediction of post-editing effort. We solve the problem of quality estimation by using a machine learning approach, where we try to learn a regressor from feature space to HTER score. By enriching the baseline features with the syntactical features and additional translation system based features, we achieve Pearson correlation of 0.525 on the test set.

Subfields: Artificial Intelligence, Information Systems, Evaluation & Benchmarks

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, background): “, 2016) UU Uppsala University, Sweden (Sagemo and Stymne, 2016) YSDA Yandex School of Data Analysis, Russia (Kozlova et al., 2016)”
Pushing the Limits of Translation Quality Estimation (2017, methodology): “report the performance of the two best systems in the sentence-level QE tasks at WMT15 and WMT16 (Bicici et al., 2015; Langlois, 2015; Kozlova et al., 2016; Kim and Lee, 2016).”
Predicting Translation Performance with Referential Translation Machines (2017, background): “• number of tokens about punctuation in the source sentence and the translation (Kozlova et al., 2016) and the cosine between them.”
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity (2017, unknown): “The new features we include are about punctuation: number of tokens about punc-Figure tuation (Kozlova et al., 2016) and the cosine be-tween the punctuation vectors.”