Challenge: Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms based on the connectivity hypothesis.
Approach: They propose a graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
Outcome: The proposed model is compared with human annotations and other automated methods on cross-linguistic supplementary adverbs.

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Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)

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Challenge: Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way.
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SMATCH++: Standardized and Extended Evaluation of Semantic Graphs (2023.findings-eacl)

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Challenge: Existing graph-alignment metrics that measure graph distances are not reliable, we show . metric is spread out and does not provide upper bounds for extended tasks.
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A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches (L18-1)

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Challenge: WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them.
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Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)

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Challenge: Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing.
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Core Semantic First: A Top-down Approach for AMR Parsing (D19-1)

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Challenge: Abstract Meaning Representation (AMR) parsing is a semantic formalism that encodes the meaning of a sentence as a rooted labeled directed graph.
Approach: They propose a scheme for parsing text into its Abstract Meaning Representation (AMR) using Graph Spanning based Parsing.
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Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)

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Challenge: Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision.
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Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)

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Challenge: Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities.
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A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024.findings-acl)

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Challenge: Existing large language models (LLMs) perform poorly in reasoning about subjective knowledge, showing strong biases and lack interpretability requirements.
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Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)

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Challenge: Pre-trained language models are used to solve tasks such as summarization and information retrieval.
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A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing (2020.lrec-1)

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Challenge: Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different approaches.
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