Viability of Neural Networks for Core Technologies for ResourceScarce Languages In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resourcescarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resourcescarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.

LowResource Text Classification Using DomainAdversarial Learning Deep learning techniques have recently shown to be successful in many natural language processing tasks forming stateoftheart systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domainadversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural network in lowresource and zeroresource settings in new target domains or languages. In case of new languages, we show that monolingual wordvectors can be directly used for training without prealignment. Their projection into a common space can be learnt adhoc at training time reaching the final performance of pretrained multilingual wordvectors.