T5: Representation, Learning and Reasoning on Spatial Language for Down-stream NLP Tasks
Parisa Kordjamshidi, James Pustejovsky, Marie-Francine Moens
Live Session 1: Nov 20, Live Session 1: Nov 20 (17:00-18:00 UTC) [Join Zoom Meeting]
Live Session 2: Nov 21, Live Session 2: Nov 21 (00:00-01:00 UTC) [Join Zoom Meeting]
Abstract: Understating spatial semantics expressed in natural language can become highly complex in real-world applications. This includes applications of language grounding, navigation, visual question answering, and more generic human-machine interaction and dialogue systems. In many of such downstream tasks, explicit representation of spatial concepts and relationships can improve the capabilities of machine learning models in reasoning and deep language understanding. In this tutorial, we overview the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. We discuss the recent results on the above-mentioned applications --that need spatial language learning and reasoning -- and highlight the research gaps and future directions.
|Nov 20, (17:00-18:00 UTC)||Q&A||Parisa Kordjamshidi, James Pustejovsky and Marie-Francine Moens|
|Nov 21, (00:00-01:00 UTC)||Q&A|
Information about the virtual format of this tutorial: This tutorial has a prerecorded talk on this page (see below) that you can watch anytime during the conference. It also has two live sessions that will be conducted on Zoom and will be livestreamed on this page. Additionally, it has a chat window that you can use to have discussions with the tutorial teachers and other attendees anytime during the conference.