| Challenge: | Distributional models learn representations of words from text but lack grounding or the linking of text to the non-linguistic world. |
| Approach: | They investigate the extent to which trajectories naturally encode verb semantics . they build a procedurally generated agent-object-interaction dataset and compare methods . |
| Outcome: | The proposed model can capture verb semantics by tracing trajectories and self-supervised pretraining. |
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Implicit Representations of Meaning in Neural Language Models (2021.acl-long)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
| Approach: | They propose to use text as a model to model entities and situations as they evolve throughout a discourse. |
| Outcome: | The proposed models have functional similarities to linguistic models of dynamic semantics and can be learned with only text as training data. |
A Distributional Perspective on Word Learning in Neural Language Models (2025.naacl-long)
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| Challenge: | Language models are increasingly being studied as models of human language learners. |
| Approach: | They propose a distributional approach to word learning that captures distributional knowledge and gradient preferences for the word’s appropriateness. |
| Outcome: | The proposed signatures capture knowledge of where the target word can and cannot occur as well as gradient preferences about the word’s appropriateness. |
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. |
| Approach: | They propose to model teacher-learner dynamics through natural interactions occurring between users and search engines. |
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Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches (2023.findings-emnlp)
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| Challenge: | People rely heavily on context to enrich meaning beyond what is literally said. |
| Approach: | They analyze how task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. |
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Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
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| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
| Outcome: | The proposed models exhibit better learning of syntactic categories, lexical relations, semantic features, word similarity and alignment with human neural representations. |
Learning Trajectories of Figurative Language for Pre-Trained Language Models (2025.findings-emnlp)
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| Challenge: | Figures of speech and figures of language are used in everyday communication . however, this imaginative use of words requires a solid understanding of semantics and real-world knowledge. |
| Approach: | They exploit probing tasks to analyse how NLMs recognise figurative language . they find out which layers have a better comprehension of figurativ language based on pre-training data. |
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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 . |
| Approach: | This tutorial combines a synthesis of multimodal grounding and meaning representation techniques with formal and computational models of situated reasoning. |
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Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
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The Grammar-Learning Trajectories of Neural Language Models (2022.acl-long)
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| Challenge: | In this paper, we show that neural language models with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
| Approach: | They propose to use mutual inductive bias to study linguistic representations implicit in NLMs. |
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Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that deep vision-only and language-only models project inputs into a partially aligned representational space. |
| Approach: | They investigate whether a model's representational code is semantically shared . they find that alignment peaks in mid-to-late layers of both model types . |
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