Recent work on bilingual Word Sense Disambiguation (WSD) has shown that a resource deprived language (L 1 ) can benefit from the annotation work done in a resource rich language (L 2 ) via parameter projection. However, this method assumes the presence of sufficient annotated data in one resource rich language which may not always be possible. Instead, we focus on the situation where there are two resource deprived languages, both having a very small amount of seed annotated data and a large amount of untagged data. We then use bilingual bootstrapping, wherein, a model trained using the seed annotated data of L 1 is used to annotate the untagged data of L 2 and vice versa using parameter projection. The untagged instances of L 1 and L 2 which get annotated with high confidence are then added to the seed data of the respective languages and the above process is repeated. Our experiments show that such a bilingual bootstrapping algorithm when evaluated on two different domains with small seed sizes using Hindi (L 1 ) and Marathi (L 2 ) as the language pair performs better than monolingual bootstrapping and significantly reduces annotation cost.
REF uses bilingual bootstrapping in which, a model is first trained using the seed annotated data of one language and then it is used to annotate the untagged data of other language and vice versa using parameter projection.
Together We Can: Bilingual Bootstrapping for WSD
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science