| Challenge: | Abstract Meaning Representation (AMR) annotations are directed acyclic graphs, but most probabilistic models view them as strings or trees. |
| Approach: | They show that some DAG automata cannot be made into useful probabilistic models by assigning weights to transitions. |
| Outcome: | The proposed model can't be made into useful probabilistic models by assigning weights to transitions . the proposed model is not feasible for all variants, but it is problematic for planar variants if they are not rooted . |
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| Challenge: | Semantic parsers treat graphs as strings or trees, but there is no guarantee that the output is a well-formed graph. |
| Approach: | They propose a graph-aware sequence model that utilizes a DAG grammar to guide graph generation. |
| Outcome: | The proposed model outperforms string-based and DAG-grammar models by a large margin and can guarantee the well-formed graphs. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
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Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
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Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
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Semantic graph parsing with recurrent neural network DAG grammars (D19-1)
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| Challenge: | Semantic parsing is the task of mapping natural language to machine interpretable meaning representations. |
| Approach: | They propose a graph-aware sequence model that generates only well-formed graphs . their model is based on a multilingual semantic graphbank . |
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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. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)
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| Challenge: | ACL 2025 System Demonstration Track accepted 64 papers based on reviews . short-listed 7 papers for Best System Demo award . |
| Approach: | the ACL 2025 System Demonstration Track is a conference for papers describing system demonstrations . the track received a record 187 submissions, of which 178 papers were valid with required materials . |
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PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics (2024.findings-naacl)
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| Challenge: | Existing studies have recognized hallucination as a notable concern in large autoregressive language models (LLMs). |
| Approach: | They propose a polygraph for large language models that detects "hallucination" they demonstrate that hallucination can be detected by tractable probabilistic models . |
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Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2024.emnlp-demo)
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| Challenge: | EMNLP 2024 conference on Empirical Methods in Natural Language Processing received 153 submissions . 52 submissions were selected for inclusion in the program (acceptance rate of 34%) |
| Approach: | EMNLP 2024 conference on Empirical Methods in Natural Language Processing will take place in london on november 12-16, 2024 . |
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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2024.acl-demos)
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| Challenge: | ACL 2024 System Demonstration Track invites submissions describing system demonstrations . submissions will undergo a single-blind review process . |
| Approach: | the ACL 2024 System Demonstration Track invites submissions . papers will be published in a companion volume of the conference proceedings . submissions will undergo a single-blind review process . |
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