Summary of the paper

Title Shata-Anuvadak: Tackling Multiway Translation of Indian Languages
Authors Anoop Kunchukuttan, Abhijit Mishra, Rajen Chatterjee, Ritesh Shah and Pushpak Bhattacharyya
Abstract We present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families. We analyze the relationship between translation accuracy and the language families involved. We feel that insights obtained from this analysis will provide guidelines for creating machine translation systems of specific Indian language pairs. We build phrase based systems and some extensions. Across multiple languages, we show improvements on the baseline phrase based systems using these extensions: (1) source side reordering for English-Indian language translation, and (2) transliteration of untranslated words for Indian language-Indian language translation. These enhancements harness shared characteristics of Indian languages. To stimulate similar innovation widely in the NLP community, we have made the trained models for these language pairs publicly available.
Topics Statistical and Machine Learning Methods, Other
Full paper Shata-Anuvadak: Tackling Multiway Translation of Indian Languages
Bibtex @InProceedings{KUNCHUKUTTAN14.414,
  author = {Anoop Kunchukuttan and Abhijit Mishra and Rajen Chatterjee and Ritesh Shah and Pushpak Bhattacharyya},
  title = {Shata-Anuvadak: Tackling Multiway Translation of Indian Languages},
  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}
Powered by ELDA © 2014 ELDA/ELRA