Publikation
Keep It or Not: Word Level Quality Estimation for Post-Editing
Prasenjit Basu; Santanu Pal; Sudip Kumar Naskar
In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers. Conference on Machine Translation (WMT-2018), located at EMNLP 2018, October 31 - November 1, Brussels, Belgium, Pages 759-764, Vol. 2, Association for Computational Linguistics, 10/2018.
Zusammenfassung
The paper presents our participation in the WMT 2018 shared task on word level quality estimation (QE) of machine translated (MT) text, i.e., to predict whether a word in MT output for a given source context is correctly translated and hence should be retained in the post-edited translation (PE), or not. To perform the QE task, we measure the similarity of the source context of the target MT word with the context for which the word is retained in PE in the training data. This is achieved in two different ways, using Bag-ofWords (BoW) model and Document-to-Vector (Doc2Vec) model. In the BoW model, we compute the cosine similarity while in the Doc2Vec model we consider the Doc2Vec similarity. By applying the Kneedle algorithm on the F1-mult vs.
similarity score plot, we derive the threshold based on which OK/BAD decisions are taken for the MT words. Experimental results revealed that the Doc2Vec model performs better than the BoW model on the word level QE task.
Projekte
DEEPLEE - Tiefes Lernen für End-to-End-Anwendungen in der Sprachtechnologie,
MMPE - Multi-Modale Sprachtechnologie-basierte Unterstützung für das Post-Editieren von Maschineller Übersetzung
MMPE - Multi-Modale Sprachtechnologie-basierte Unterstützung für das Post-Editieren von Maschineller Übersetzung