In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

In this article, we introduce and discuss the pervasive issue of bias in the large language
models that are currently at the core of mainstream approaches to Natural Language …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

Abstract Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word
in context by identifying the most suitable meaning from a predefined sense inventory …

Massively multilingual transformers pretrained with language modeling objectives (eg,
mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross …

The role of robots in society keeps expanding and diversifying, bringing with it a host of
issues surrounding the relationship between robots and humans. This introduction to human …

By design, word embeddings are unable to model the dynamic nature of words' semantics,
ie, the property of words to correspond to potentially different meanings. To address this …

The ability of learning from noisy labels is very useful in many visual recognition tasks, as a
vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise …

Recent evolutions in the e-commerce market have led to an increasing importance attributed
by consumers to product reviews made by third parties before proceeding to purchase. The …