Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks (2020)

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.

## outline the tutorial will cover the following syllabus: parisa @xcite . in: pustejovsky j., ide n. (eds.), handbook of linguistic annotation springer verlag. in proceedings of the thirty-second aaai conference on artificial intelligence (aaai-18) (5504-5511). https: //arxiv.org/abs/1707.03804 • acquiring common sense spatial knowledge through implicit spatial templates. guillem collell, luc van gool and marie-francine moens (2018). in proceedings of the thirty-second aaai conference on artificial intelligence (aaai 2018) (pp. 6765-6772). aaai. https://arxiv.org/abs/1711.06821 • generating a novel dataset of multimodal referring expressions. nikhil krishnaswamy, and james pustejovsky. in proceedings of the 13th international conference on computational semantics, pp. 44-51. 2019. https://www.aclweb.org/ anthology/w19-0507.pdf 4 instructors • parisa kordjamshidi is assistant professor of computer science department at michigan state university. her research interests are in nlp and machine learning. she has been working on spatial semantics extraction and annotation schemes, mapping language to formal spatial representations, spatial ontologies, structured output prediction models for information extraction, combining vision and language for spatial language understanding. she has been organizing/co-organizing shared tasks on spatial role labeling, sprl-2012, sprl-2013 and the space evaluation workshop, spaceeval-2015, in semeval series and multimodal spatial role labeling workshop msprl at clef-2017 with the goal of considering vision and language media for spatial information extraction and organized splu-2018 and robonlp-splu collocated with naacl-18 and naacl-2019 respectively.