NeuMorph

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

Surfing the Modeling of pos Taggers in Low-Resource Scenarios

“…On the other, the scarcity of training data is not only problematic per se, but also because of its impact on the rest of trials. So, generating high-quality vector representations remains a challenge [26] and the imbalance in the training samples that start the long tail and bias phenomena is more likely, together with the proneness to overfitting of DL models [27], which can result in poor predictive power, thereby compromising both inference and decision making.…”

“…These encompass a class of NLP problems that involve the assignment of a categorical label to each member of a sequence of observed values, and whose output facilitates downstream applications, such as parsing or semantic analysis, so errors at this stage can lower their performance [31]. Among the most important, we can highlight named entity recognition [7,32], multi-word expression identification [29], and morphological [26] and POS tagging [2,[33][34][35][36]. It is precisely in this framework, the generation of POS taggers for low-resource scenarios by means of non-deep ML, that we propose the study of model selection based on the early estimation of learning curves.…”