Papers with TAG
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
Strong Equivalence of TAG and CCG (2021.tacl-1)
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| Challenge: | Tree-adjoining grammar and combinatory categorial grammar have the same expressive power on trees. |
| Approach: | Tree-adjoining grammar (TAG) and combinatory categorial grammar (CCG) are well-established grammars with the same expressive power on strings. |
| Outcome: | The proposed grammars have the same expressive power on trees as classical grammars and can express a limited amount of cross-serial dependencies and have the constant growth property. |
End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)
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| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
| Outcome: | The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points. |
Text Annotation Graphs: Annotating Complex Natural Language Phenomena (L18-1)
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| Challenge: | Text Annotation Graphs is a web-based tool for annotating text . it provides functionality for representing complex relationships between words and word phrases . |
| Approach: | They introduce a web-based tool for annotating text, Text Annotation Graphs, or TAG . it provides functionality for representing complex relationships between words and word phrases . |
| Outcome: | The proposed software can represent complex relationships between words and words . it can also be used to find similar structures within the current document or external annotated documents. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
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| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)
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Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party. |
| Approach: | They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks. |
| Outcome: | The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label. |
Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages (2023.emnlp-main)
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| Challenge: | a class of tree-adjoining languages can be characterized by various two-level formalisms controlled by semiring-weighted CFGs and PDAs. |
| Approach: | They propose semiring-weighted versions of controllable CFGs and PDAs . they also introduce a WPDA normal form that is analogous to Chomsky's normal form for CFG . |
| Outcome: | The proposed algorithms are more time-efficient than the previous ones for LIG, PAA, and EPDA. |
Convergence and Diversity in the Control Hierarchy (2023.acl-long)
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| Challenge: | Weir has defined a hierarchy of language classes whose second member (L2) is generated by tree-adjoining grammars (TAG), linear indexed grammars, combinatory categorial grammars and head grammars. |
| Approach: | They propose to extend Weir's mechanism of control to give a definition of controllable pushdown automata (PDAs) they propose to use a stricter notion of equivalence to allow for finer-grained comparisons than weak equvalence. |
| Outcome: | The proposed language classes are d-weakly equivalent to Weir's original two-level grammar, but not d strongly equivalent. |
A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)
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| Challenge: | Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability. |
| Approach: | They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor. |
| Outcome: | The proposed method outperforms baseline methods by 6%-16% in F1 scores. |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)
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| Challenge: | Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness. |
| Approach: | They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL. |
| Outcome: | The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side. |
TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing models for protecting public figures from online abuse ignore who is targeted and how. |
| Approach: | They propose a target-aware multi-task framework that conditions downstream predictions on upstream beliefs via three lightweight modules: Cross-Task Feature Fusion (CTF), Task-Adaptive Gating (TAG), and Label-Guided Span Detection (LGSD). |
| Outcome: | The proposed framework yields higher average F1 than single-task training and standard multi-task learning. |
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| Challenge: | Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency. |
| Approach: | They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them. |
| Outcome: | The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens. |