We present the first shared task on Semantic Textual Relatedness (STR). While earlier
shared tasks primarily focused on semantic similarity, we instead investigate the broader …

Pre-trained transformer models shine in many natural language processing tasks and
therefore are expected to bear the representation of the input sentence or text meaning …

Abstract Word Usage Graphs (WUGs) represent human semantic proximity judgments for
pairs of word uses in a weighted graph, which can be clustered to infer word sense clusters …

Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of
similarity between a pair of sentences, and has broad application in fields such as …

Despite their remarkable successes, state-of-the-art large language models (LLMs),
including vision-and-language models (VLMs) and unimodal language models (ULMs), fail …

One of the major outstanding questions in computational semantics is how humans integrate
the meaning of individual words into a sentence in a way that enables understanding of …

Word and sentence embeddings are useful feature representations in natural language
processing. However, intrinsic evaluation for embeddings lags far behind, and there has …

Semantic textual relatedness is a broader concept of semantic similarity. It measures the
extent to which two chunks of text convey similar meaning or topics, or share related …

Words play a central role in how we express ourselves. Lexicons of word-emotion
associations are widely used in research and real-world applications for sentiment analysis …

Semantic Textual Relatedness holds significant relevance in Natural Language Processing,
finding applications across various domains. Traditionally, approaches to STR have relied …