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OpenKiwi: An Open Source Framework for Quality Estimation

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arxiv 1902.08646 v2 pith:RNSG4ZUX submitted 2019-02-22 cs.CL cs.LG

Fábio Kepler , Jonay Trénous , Marcos Treviso , Miguel Vera , André F. T. Martins This is my paper · ORCID
classification cs.CLcs.LG
keywords estimationopenkiwiqualityframeworkopensentence-levelsourcestate-of-the-art
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We introduce OpenKiwi, a PyTorch-based open source framework for translation quality estimation. OpenKiwi supports training and testing of word-level and sentence-level quality estimation systems, implementing the winning systems of the WMT 2015-18 quality estimation campaigns. We benchmark OpenKiwi on two datasets from WMT 2018 (English-German SMT and NMT), yielding state-of-the-art performance on the word-level tasks and near state-of-the-art in the sentence-level tasks.

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  1. The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation

    cs.CL 2019-08 conditional novelty 4.0 of 10

    For similar language pairs, phrase-based statistical and neural translation produce comparable BLEU scores, with the better system depending on the specific language pair.

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