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Proceedings of the 28th International Conference on Computational Linguistics
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. However, most of the prior work on this topic has focused on high resource languages. In this paper, we evaluate cross-lingual approaches for low resource languages, especially in the context of morphologically rich Indian languages. We test our model on six languages from two different families and develop linguistic insights into each model's performance. Models We adapt thedoi:10.18653/v1/2020.coling-main.534 fatcat:phiuzotopbbs3frlu4fdzt2bna