Statistical Machine Translation (SMT) approaches fails to handle the rich morphology when translating into morphologically rich languages. This is due to the data sparsity, which is the missing of the morphologically inflected forms of words from the parallel corpus. We investigated a method to generate these unseen morphological forms. In this paper, we analyze the morphological complexity of a morphologically rich Indian language Malayalam when translating from English. Being a highly agglutinative language, it is very difficult to generate the various morphological inflected forms for Malayalam. We study both the factor based models and the phrase based models and the problem of data sparseness. We propose a simple and effective solution based on enriching the parallel corpus with generated morphological forms. We verify this approach with various experiments on English-Malayalam SMT. We observes that the morphology injection method improves the quality of the translation. We have analyzed the experimental results both in terms of automatic and subjective evaluations.
@InProceedings{S18.125, author = {Sreelekha S and Pushpak Bhattacharyya}, title = "{Morphology Injection for English-Malayalam Statistical Machine Translation}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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