Papers with structural complexity
Towards Unification of Discourse Annotation Frameworks (2022.acl-srw)
Copied to clipboard
| Challenge: | Discourse information is difficult to represent and annotate, and corpora annotated under different frameworks vary considerably. |
| Approach: | They propose to use automatic means to unify discourse structures and relations . they will also explore the application of the unified framework in multi-task learning and graphical models . |
| Outcome: | The proposed method can be used in multi-task learning and graphical models. |
Cross-Lingual Transfer of Cognitive Processing Complexity (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. |
| Approach: | They propose a multilingual model that uses structural similarities between languages to facilitate cross-lingual transfer by a meaningful bias towards sentence length and cross-linguistic differences. |
| Outcome: | The proposed model can predict varied patterns for 13 languages, despite being fine-tuned only on English data. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
Copied to clipboard
| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current evaluations of RAG systems overlook structural complexity and multi-step reasoning . GRADE model enables fine-grained analysis of Ragging performance . |
| Approach: | They propose a framework that models retrieval difficulty along two orthogonal dimensions . they extract knowledge graphs and augment them through semantic clustering to recover missing links . |
| Outcome: | The proposed framework models retrieval difficulty along two orthogonal dimensions . error rates correlate with the framework, and it validates its diagnostic utility. |
In-Context Compositional Generalization for Large Vision-Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that in-context learning for large language models exhibits compositional generalization capacity. |
| Approach: | They propose a method to exhibit in-context compositional generalization in large vision-language models by combining visual and linguistic modalities. |
| Outcome: | The proposed method reduces redundancy and complexity in in-context learning with LVLMs. |
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility. |
| Approach: | They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem. |
| Outcome: | The proposed framework captures the trade-off between information relevance and structural complexity. |
Hallucination Detection in Long-Form Text Generated by LLMs: A Benchmark and a Hyper-Relational Knowledge Graph Approach (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection are coarse-grained and lack long-range consistency checks. |
| Approach: | They propose a benchmark for long-form hallucination detection that incorporates diverse entity types and intricate factual dependencies spanning extended contexts. |
| Outcome: | The proposed framework outperforms baselines and robustly integrates fact-centric hyper-relational knowledge graphs. |
TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity (2026.findings-acl)
Copied to clipboard
Zheyuan Yang, Liqiang Shang, Junjie Chen, Xun Yang, Chenglong Xu, Bo Yuan, Chenyuan Jiao, Yaoru Sun, Yilun Zhao
| Challenge: | TableVista evaluates multimodal table reasoning under visual and structural complexity . current models struggle to maintain reasoning consistency when structural complexity combined with visually integrated presentations. |
| Approach: | They propose a benchmark for evaluating multimodal table reasoning under visual and structural complexity. |
| Outcome: | The proposed model performs poorly on visual and structural complexity. |