The disparity in the languages commonly studied in Natural Language Processing (NLP) is
typically reflected by referring to languages as low vs high-resourced. However, there is …

Unsupervised bilingual lexicon induction (BLI) task aims to find word translations between
languages and has achieved great success in similar language pairs. However, related …

The ability to extract high-quality translation dictionaries from monolingual word embedding
spaces depends critically on the geometric similarity of the spaces--their degree of" …

Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-
resource languages lack labeled data for training. An established method for training a POS …

Cross-lingual word embeddings (CLWEs) have proven indispensable for various natural
language processing tasks, eg, bilingual lexicon induction (BLI). However, the lack of data …

In this paper, we present a straightforward technique for constructing interpretable word
embeddings from morphologically analyzed examples (such as interlinear glosses) for all of …

Dictionary-based data augmentation techniques have been used in the field of domain
adaptation to learn words that do not appear in the parallel training data of a machine …

Very low-resource languages, having only a few million tokens worth of data, are not well-
supported by multilingual NLP approaches due to poor quality cross-lingual word …

The importance of inducing bilingual dictionary components in many natural language
processing (NLP) applications is indisputable. However, the dictionary compilation process …

To date, most work towards developing natural language processing (NLP) technologies
has focused on the English language. At the same time, there are an estimated 7000+ living …