Referential Ambiguity

Referential ambiguity, the phenomenon where a word or phrase can refer to multiple entities, is a significant challenge in natural language processing (NLP). Current research focuses on improving computational models' ability to resolve these ambiguities, particularly in dialogue and multimodal contexts, using techniques that incorporate contextual information and leverage insights from cognitive science, such as pragmatic reasoning and pedagogical approaches. This work aims to create more robust and human-like NLP systems by developing better methods for disambiguation, which has implications for improving human-computer interaction, machine translation, and other applications requiring natural language understanding. The development of new benchmark datasets and diagnostic corpora is also a key area of focus, enabling more rigorous evaluation of model performance.