An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …

Cross-lingual transfer, where a high-resource transfer language is used to improve the
accuracy of a low-resource task language, is now an invaluable tool for improving …

This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …

We propose a new formulation for multilingual entity linking, where language-specific
mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this …

Supervised machine learning has several drawbacks that make it difficult to use in many
situations. Drawbacks include heavy reliance on massive training data, limited …

In existing sophisticated text-to-SQL models, schema linking is often considered as a simple,
minor component, belying its importance. By providing a schema linking corpus based on …

Transfer learning based on pretraining language models on a large amount of raw data has
become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear …

Massively multilingual pre-trained language models, such as mBERT and XLM-RoBERTa,
have received significant attention in the recent NLP literature for their excellent capability …

Large pre-trained language models (LMs) have demonstrated the ability to obtain good
performance on downstream tasks with limited examples in cross-lingual settings. However …

Relation classification aims to extract semantic relations between entity pairs from the
sentences. However, most existing methods can only identify seen relation classes that …