Summary of the paper
| Title | An Efficient and User-friendly Tool for Machine Translation Quality Estimation |
|---|---|
| Authors | Kashif Shah, Marco Turchi and Lucia Specia |
| Abstract | We present a new version of QUEST ― an open source framework for machine translation quality estimation ― which brings a number of improvements: (i) it provides a Web interface and functionalities such that non-expert users, e.g. translators or lay-users of machine translations, can get quality predictions (or internal features of the framework) for translations without having to install the toolkit, obtain resources or build prediction models; (ii) it significantly improves over the previous runtime performance by keeping resources (such as language models) in memory; (iii) it provides an option for users to submit the source text only and automatically obtain translations from Bing Translator; (iv) it provides a ranking of multiple translations submitted by users for each source text according to their estimated quality. We exemplify the use of this new version through some experiments with the framework. |
| Topics | Evaluation Methodologies, Machine Translation, SpeechToSpeech Translation |
Full paper  |
An Efficient and User-friendly Tool for Machine Translation Quality Estimation |
| Bibtex | @InProceedings{SHAH14.964, author = {Kashif Shah and Marco Turchi and Lucia Specia}, title = {An Efficient and User-friendly Tool for Machine Translation Quality Estimation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |