Recent advances in text mining have provided new methods for capitalizing on the
voluminous natural language text data created by organizations, their employees, and their …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

Word embeddings are well known to capture linguistic regularities of the language on which
they are trained. Researchers also observe that these regularities can transfer across …

In cross-lingual transfer, NLP models over one or more source languages are applied to a
low-resource target language. While most prior work has used a single source model or a …

Abstract We present Attract-Repel, an algorithm for improving the semantic quality of word
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …

Large pretrained language models (LMs) have become the central building block of many
NLP applications. Training these models requires ever more computational resources and …

The performance of multilingual pretrained models is highly dependent on the availability of
monolingual or parallel text present in a target language. Thus, the majority of the world's …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …