Summary of the paper
| Title | Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT |
|---|---|
| Authors | Kunal Sachdeva, Rishabh Srivastava, Sambhav Jain and Dipti Sharma |
| Abstract | Recent studies in machine translation support the fact that multi-model systems perform better than the individual models. In this paper, we describe a Hindi to English statistical machine translation system and improve over the baseline using multiple translation models. We have considered phrase based as well as hierarchical models and enhanced over both these baselines using a regression model. The system is trained over textual as well as syntactic features extracted from source and target of the aforementioned translations. Our system shows significant improvement over the baseline systems for both automatic as well as human evaluations. The proposed methodology is quite generic and easily be extended to other language pairs as well. |
| Topics | Statistical and Machine Learning Methods, Tools, Systems, Applications |
Full paper  |
Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT |
| Bibtex | @InProceedings{SACHDEVA14.682, author = {Kunal Sachdeva and Rishabh Srivastava and Sambhav Jain and Dipti Sharma}, title = {Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT}, 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} } |