Summary of the paper
| Title | A Dataset for Assessing Machine Translation Evaluation Metrics |
|---|---|
| Authors | Lucia Specia, Nicola Cancedda and Marc Dymetman |
| Abstract | We describe a dataset containing 16,000 translations produced by four machinetranslation systems and manually annotated for quality by professionaltranslators. This dataset can be used in a range of tasks assessing machinetranslation evaluation metrics, from basic correlation analysis to training andtest of machine learning-based metrics. By providing a standard dataset forsuch tasks, we hope to encourage the development of better MT evaluationmetrics. |
| Language | Statistical and machine learning methods |
| Topics | Corpus (creation, annotation, etc.), Machine Translation, SpeechToSpeech Translation, Statistical and machine learning methods |
Full paper  |
A Dataset for Assessing Machine Translation Evaluation Metrics |
| Bibtex | @InProceedings{SPECIA10.504, author = {Lucia Specia, Nicola Cancedda and Marc Dymetman}, title = {A Dataset for Assessing Machine Translation Evaluation Metrics}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |