Automated Adaptation Between Kiranti Languages

McCloy, Daniel, M.A., December 2006 Linguistics Automated Adaptation Between Kiranti Languages Chairperson: Dr. Anthony Mattina Minority language communities that are seeking to develop their language may be hampered by a lack of vernacular materials. Large volumes of such materials may be available in a related language. Automated adaptation holds potential to enable these large volumes of materials to be efficiently translated into the resource-scarce language. I describe a project to assess the feasibility of automatically adapting text between Limbu and Yamphu, two languages in Nepal’s Kiranti grouping. The approaches taken—essentially a transfer-based system partially hybridized with a Kiranti-specific interlingua—are placed in the context of machine translation efforts world-wide. A key principle embodied in this strategy is that adaptation can transcend the structural obstacles by taking advantage of functional commonalities. That is, what matters most for successful adaptation is that the languages “care about the same kinds of things.” I examine various typological phenomena of these languages to assess this degree of functional commonality. I look at the types of features marked on the finite verb, case-marking systems, the encoding of vertical deixis, object-incorporated verbs, and nominalization issues. As this Kiranti adaptation goal involves adaptation into multiple target languages, I also present a disambiguation strategy that ensures that the manual disambiguation performed for one target language is fed back into the system, such that the same disambiguation will not need to be performed again for other target languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen