Papers with graph-based method
HiPool: Modeling Long Documents Using Graph Neural Networks (2023.acl-short)
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| Challenge: | Recent work on pretraining languages have achieved satisfying results in many NLP tasks, but they are still restricted by a pre-defined maximum length. |
| Approach: | They propose a graph-based method to model sentence-level information using a fixed length and graphs to model intra- and cross-sentence correlations. |
| Outcome: | The proposed model outperforms baseline models by 2.6% in F1 score, and 4.8% on the longest sequence dataset. |
HiGraAgent: Dual-Agent Adaptive Reasoning over Hierarchical Knowledge Graph for Open Domain Multi-hop Question Answering (2026.findings-eacl)
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| Challenge: | Existing approaches to multi-hop question answering lack a robust and flexible approach to QA . prior work showed compositionality gap persists even for Large Language Models . |
| Approach: | They propose a framework that unifies graph-based retrieval with adaptive reasoning . HiGraAgent uses a hierarchical knowledge Graph with entity alignment . |
| Outcome: | The proposed framework outperforms the strongest graph-based method on hotpotQA, 2WikiMultihopQA, and MuSiQue. |
Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)
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| Challenge: | Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs. |
| Approach: | They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings. |
Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)
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| Challenge: | Existing methods for decoding dependency trees are 10 times faster than current ones. |
| Approach: | They propose a graph-based method to tackle a dependency tree linearization task . they propose to solve a Traveling Salesman Problem and combine the solution into a projective tree . |
| Outcome: | The proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding. |
Rethinking Sentiment Style Transfer (2021.findings-emnlp)
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| Challenge: | Existing evaluation methods for text style transfer are unsatisfactory. |
| Approach: | They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals. |
| Outcome: | The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation. |
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)
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| Challenge: | Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations. |
| Approach: | They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models. |
| Outcome: | The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets. |
Heterogeneous Graph Neural Networks for Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches for keyphrase generation generate uncontrollable and inaccurate absent keyphrases. |
| Approach: | They propose a graph-based method that captures explicit knowledge from related references. |
| Outcome: | The proposed model improves on baseline keyphrase generation models on multiple benchmarks. |
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (2021.acl-long)
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| Challenge: | Existing models for ECE tend to explore relative position information and suffer from the dataset bias. |
| Approach: | They propose to generate adversarial examples where relative position is no longer indicative feature of cause clauses to address the dataset bias. |
| Outcome: | The proposed method performs on par with existing state-of-the-art methods on the original ECE dataset and is more robust against adversarial attacks compared to existing models. |
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing studies focus on frame semantic parsing as a graph construction problem. |
| Approach: | They propose an end-to-end neural model to tackle frame semantic parsing jointly. |
| Outcome: | The proposed model is highly competitive and performs better than pipeline models on two benchmark datasets. |
Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus (2022.lrec-1)
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| Challenge: | Word embedding methods use word co-occurrences to encode, syntactic and semantic information to describe vocabulary in a low-dimensional space. |
| Approach: | They evaluate word embedding interpretability using two methods . they use a word-in-space vector encoder and graph-based method SPINE . |
| Outcome: | The proposed methods show that they can be interpretable on a large French corpus. |
Dependency Parsing via Sequence Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method. |
| Approach: | They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. |
| Outcome: | The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16. |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
SenSALDO: Creating a Sentiment Lexicon for Swedish (L18-1)
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| Challenge: | sentiment analysis has seen an explosive expansion over the last decade or so . many theoretical and methodological questions remain unanswered and resource gaps unfilled . |
| Approach: | They develop a sentiment lexicon for written (standard) Swedish using an existing dataset . they assign a real value sentiment score in the range [-1,1] and produce a label for it . |
| Outcome: | The proposed sentiment lexicon is an open source resource from the Swedish Language Bank . it is based on an existing gold standard dataset and is available from Sprkbanken . |