Deep learning has recently come to dominate computational linguistics, leading to claims of
human-level performance in a range of language processing tasks. Like much previous …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

In recent years, neural networks have shown impressive performance gains on long-
standing AI problems, such as answering queries from text and machine translation. These …

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic
information, resulting in state-of-the-art performance on various tasks. We propose a very …

There is a growing body of research demonstrating that the cerebellum is involved in
language understanding. Early theories assumed that the cerebellum is involved in low …

An increasing number of machine learning models have been deployed in domains with
high stakes such as finance and healthcare. Despite their superior performances, many …

Background Semantic textual similarity (STS) is a natural language processing (NLP) task
that involves assigning a similarity score to 2 snippets of text based on their meaning. This …

Knowledge representation has gained in relevance as data from the ubiquitous digitization
of behaviors amass and academia and industry seek methods to understand and reason …

Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …