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.
Highlights
We overview the state-of-the-art for extraction of spatial information from language, both the abstract semantic extraction (Kordjamshidi et al, 2011; Kordjamshidi and Moens, 2015) and extraction that is driven by various target tasks and applications
We overview the usage of spatial semantics by various downstream tasks and killer applications including language grounding, navigation, self-driving cars, robotics (Tellex et al, 2011; Kollar et al, 2010), dialogue systems (Kelleher and Kruijff, 2006) and human machine interaction, and geographical information systems and knowledge graphs (Stock et al, 2013; Mai et al, 2020)
Throughout this proposal, we will highlight the importance of combining learning and reasoning for spatial language understanding and its influence on the semantic representation and type of the learning models as well as the performance on various applications
Summary
This tutorial provides an overview over the cutting edge research on spatial language understanding. While formal meaning representation is a general issue for language understanding, formalizing spatial concepts and building formal reasoning and machine learning models based on those constitute challenging research problems with a wealth of prior foundational work that can be exploited and linked to language understanding In this tutorial, we overview four themes: 1) Spatial Semantic Representation; 2) Spatial Information Extraction and; 3) Spatial qualitative representation and reasoning 4) Downstream applications of spatial semantic extraction and spatial reasoning including language grounding, robotics, navigation, dialogue systems and tasks that require combining vision and language. The main goal of this tutorial is to combine these current related efforts from different communities and application domains into one unified treatment, to identify the challenges, problems and future directions for spatial language understanding
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