3 accepted papers
Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative, sequence-to-sequence framework that perform
The Knowledge-Aware Visual Question Answering about Entity task aims to disambiguate entities using textual and visual information, as well as knowledge. It usually relies on two independent steps, information retrieval then reading comprehension, that do not benefit each other. Retrieval Augmented…
In this paper, we propose a probabilistic MLN-based model for spatial context disambiguation. This model serves as a solution for the problem of incomplete knowledge in High-level task planning. By applying the state of the art MLN probabilistic reasoning such as MCSAT, we determine the concept clas…