6 papers
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Maggie Mi, Golzar Atefi, Atsuki Yamaguchi +3
Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntact…
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras
Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning,…
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors
Maggie Mi, Aline Villavicencio, Nafise Sadat Moosavi
Language models often struggle with idiomatic, figurative, or context-sensitive inputs, not because they produce flawed outputs, but because they misinterpret the input from the ou…
Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection
Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith +3
The recent trend towards utilisation of reasoning models has improved the performance of Large Language Models (LLMs) across many tasks which involve logical steps. One linguistic…
Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context
Maggie Mi, Aline Villavicencio, Nafise Sadat Moosavi
Human processing of idioms relies on understanding the contextual sentences in which idioms occur, as well as language-intrinsic features such as frequency and speaker-intrinsic fa…
SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation
Thomas Pickard, Aline Villavicencio, Maggie Mi +3
Idiomatic expressions present a unique challenge in NLP, as their meanings are often not directly inferable from their constituent words. Despite recent advancements in Large Langu…