Summary of the paper
| Title | Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System |
|---|---|
| Authors | Claire Jaja, Douglas Briesch, Jamal Laoudi and Clare Voss |
| Abstract | Custom machine translation (MT) engines systematically outperform general-domain MT engines when translating within the relevant custom domain. This paper investigates the use of the Jensen-Shannon divergence measure for automatically routing new documents within a translation system with multiple MT engines to the appropriate custom MT engine in order to obtain the best translation. Three distinct domains are compared, and the impact of the language, size, and preprocessing of the documents on the Jensen-Shannon score is addressed. Six test datasets are then compared to the three known-domain corpora to predict which of the three custom MT engines they would be routed to at runtime given their Jensen-Shannon scores. The results are promising for incorporating this divergence measure into a translation workflow. |
| Topics | Document Classification, Text categorisation, Machine Translation, SpeechToSpeech Translation, Tools, systems, applications |
Full paper  |
Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System |
| Bibtex | @InProceedings{JAJA12.843, author = {Claire Jaja and Douglas Briesch and Jamal Laoudi and Clare Voss}, title = {Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |