Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a
few visits per patient and a few observations per disease can be exploited. Although meta …

Recent research considers few-shot intent detection as a meta-learning problem: the model
is learning to learn from a consecutive set of small tasks named episodes. In this work, we …

An increasing number of people in the world today speak a mixed-language as a result of
being multilingual. However, building a speech recognition system for code-switching …

Taxonomies are symbolic representations of hierarchical relationships between terms or
entities. While taxonomies are useful in broad applications, manually updating or …

The term low-resourced has been tossed around in the field of natural language processing
to a degree that almost any language that is not English can be called" low-resourced"; …

We consider two problems of NMT domain adaptation using meta-learning. First, we want to
reach domain robustness, ie, we want to reach high quality on both domains seen in the …

Abstract Modeling hypernym-hyponym (“is-a”) relations is very important for many natural
language processing (NLP) tasks, such as classification, natural language inference and …

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of
large annotated datasets with sufficient coverage of words in their diversity of senses. This …

The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news
headlines highlights the urgent need for effective ways to detect it. Currently, researchers …

First-order meta-learning algorithms have been widely used in practice to learn initial model
parameters that can be quickly adapted to new tasks due to their efficiency and …