Kunal Sachdeva


Exploring the effect of semantic similarity for Phrase-based Machine Translation
Kunal Sachdeva | Dipti Sharma
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality


Reducing the Impact of Data Sparsity in Statistical Machine Translation
Karan Singla | Kunal Sachdeva | Srinivas Bangalore | Dipti Misra Sharma | Diksha Yadav
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT
Kunal Sachdeva | Rishabh Srivastava | Sambhav Jain | Dipti Sharma
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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.