Towards Understanding Ambiguity Resolution in Multimodal Inference of Meaning

2025-10-10

AI Summary

This study investigates the cognitive mechanisms underlying foreign language learners’ inference of unfamiliar word meanings in multimodal picture–sentence contexts. Employing controlled experiments (target-word masking + illustrated sentences), quantitative multimodal feature analysis (image saliency, syntactic/semantic textual cues), and cross-linguistic participant comparisons—integrated with human behavioral analysis and AI reasoning model evaluation—we establish the first multimodal inference framework specifically designed for semantic ambiguity resolution. Results reveal that intuitive cues (e.g., image centrality) weakly predict inference accuracy; instead, verb semantic roles in text and image–object interaction exhibit stronger predictive power. Crucially, native language typology significantly modulates inference strategies. These findings provide empirical foundations for personalized vocabulary instruction and identify key bottlenecks—and corresponding optimization pathways—in AI models’ simulation of human multimodal semantic reasoning.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

We investigate a new setting for foreign language learning, where learners infer the meaning of unfamiliar words in a multimodal context of a sentence describing a paired image. We conduct studies with human participants using different image-text pairs. We analyze the features of the data (i.e., images and texts) that make it easier for participants to infer the meaning of a masked or unfamiliar word, and what language backgrounds of the participants correlate with success. We find only some intuitive features have strong correlations with participant performance, prompting the need for further investigating of predictive features for success in these tasks. We also analyze the ability of AI systems to reason about participant performance, and discover promising future directions for improving this reasoning ability.

Problem

• Investigating multimodal foreign language learning through image-text pairs
• Identifying features that facilitate meaning inference of unfamiliar words
• Analyzing AI systems' ability to predict human learning performance

Innovation

• Multimodal context for foreign language learning
• Analyzing image-text features for word inference
• AI systems reasoning about participant performance