NeuMorph: Neural Morphological Tagging for Low-Resource Languages—An Experimental Study for Indic Languages
Abstract
This article deals with morphological tagging for low-resource languages. For this purpose, five Indic languages are taken as reference.
In addition, two severely resource-poor languages, Coptic and Kurmanji, are also considered. The task entails prediction of the morphological tag (case, degree, gender, etc.) of an in-context word.
We hypothesize that to predict the tag of a word, considering its longer context such as the entire sentence is not always necessary. In this light, the usefulness of convolution operation is studied resulting in a convolutional neural network (CNN) based morphological tagger.
Our proposed model (BLSTM-CNN) achieves insightful results in comparison to the present state-of-the-art. Following the recent trend, the task is carried out under three different settings: single language, across languages, and across keys.
Whereas the previous models used only character-level features, we show that the addition of word vectors along with character-level embedding significantly improves the performance of all the models. Since obtaining high-quality word vectors for resource-poor languages remains a challenge, in that scenario, the proposed character-level BLSTM-CNN proves to be most effective.1