| Challenge: | Despite the rapid progress in NLU, current systems lack the rich mental representations that people use for language understanding. |
| Approach: | They propose an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL) they propose a system architecture along with a roadmap towards realizing this vision. |
| Outcome: | The proposed approach will improve the performance of existing systems and provide a roadmap towards realizing this vision. |
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| Challenge: | embodied language learning is a form of language understanding where the language learner is situated in the world, perceives it, and interacts with it. |
| Approach: | They propose to use a concept of World Scopes to measure progress in language understanding research. |
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Grounding Meaning Representation for Situated Reasoning (2022.aacl-tutorials)
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| Challenge: | a tutorial aims to build agents that understand language using a simulated environment . situated reasoning is a critical aspect of human language understanding . |
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LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models (2023.findings-acl)
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| Challenge: | Figures are based on the use of words in a way that deviates from their conventional order and meaning. |
| Approach: | They propose to use a figurative language model to interpret embodied metaphors by using larger language models that conceptualise embodies the action of the metaphorical sentence. |
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Bridging Perception, Memory, and Inference through Semantic Relations (2021.emnlp-main)
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| Challenge: | Recent studies suggest that it is impossible to learn meaning from surface form alone. |
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Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)
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| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
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Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models? (2025.findings-emnlp)
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| Challenge: | Despite significant progress in multimodal language models, it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models. |
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tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)
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| Challenge: | Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language. |
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Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (2020.acl-main)
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| Challenge: | a priori, large neural language models are described as understanding or capturing meaning on tasks that are ostensibly meaningsensitive. |
| Approach: | They argue that a system trained only on form has no way to learn meaning . they argue that this is due to a misunderstanding of the relationship between form and meaning - which is a misconception in NLP . |
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Can Language Models Understand Physical Concepts? (2023.emnlp-main)
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| Challenge: | Existing language models do not understand basic physical concepts in the human world. |
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Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks (2020.emnlp-tutorials)
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| Challenge: | In this tutorial, we discuss 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. |
| Approach: | This tutorial presents cutting-edge research results and current 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. |
| Outcome: | This paper reviews the cutting-edge research results and current 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. |