Q&A platforms have been crucial for the online help-seeking behavior of programmers.
However, the recent popularity of ChatGPT is altering this trend. Despite this popularity, no …

Recent years have seen an exponential growth (+ 98% in 2022 wrt the previous year) of the
number of research articles in the few-shot learning field, which aims at training machine …

Over the last few years, deep learning has revolutionized the field of machine learning by
dramatically improving the state-of-the-art in various domains. However, as the size of …

Named Entity Recognition (NER) performance often degrades rapidly when applied to target
domains that differ from the texts observed during training. When in-domain labelled data is …

Machine learning (ML) has become increasingly prevalent in various domains. However, ML
algorithms sometimes give unfair outcomes and discrimination against certain groups …

Learning from multiple annotators took a valuable step towards modelling data that does not
fit the usual single annotator setting. However, multiple annotators sometimes offer varying …

In recent times, a multitude of knowledge-based applications draw knowledge from diverse
sources to cater to escalating task demands. However, the presence of errors or …

Despite sequences being core to NLP, scant work has considered how to handle noisy
sequence labels from multiple annotators for the same text. Given such annotations, we …

Knowledge verification is an important task in the quality management of knowledge graphs
(KGs). Knowledge is a summary of facts and events based on human cognition and …

The evaluation of knowledge quality (KQ) in multisource knowledge graphs (KGs) is an
essential step for many applications, such as fragmented knowledge fusion and knowledge …