Papers with graph-based methods
Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection (2024.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for code clone detection are fast and scalable, but they may fail to detect clones that have different syntax or structure. |
| Approach: | They propose to use graph-based methods for code clone detection instead of sequence-based models. |
| Outcome: | The proposed graph-based methods outperforms sequence-based models on cross-lingual code clones. |
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation (2022.naacl-srw)
Copied to clipboard
| Challenge: | Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). |
| Approach: | They propose a graph-based ERC model which considers conversational context and speaker personality. |
| Outcome: | The proposed model outperforms baseline and other graph-based methods on a benchmark dataset. |
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization (2025.naacl-short)
Copied to clipboard
| Challenge: | Graph-based methods for extracting documents have been popular, but they often require external tools or additional machine learning models to define graph components. |
| Approach: | They propose a heterogeneous graph construction for extractive summarization that defines two levels of information and four types of edges without any need for auxiliary learning models. |
| Outcome: | The proposed graph construction outperforms previous graph-based models on two datasets and is available on GitHub. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
Copied to clipboard
Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them. |
| Approach: | They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular benchmark datasets. |
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model (2021.acl-long)
Copied to clipboard
| Challenge: | Existing graph-based constituent parsing methods generate hidden nodes with the dummy label inside the n-ary nodes to transform the tree into a binary tree for prediction. |
| Approach: | They propose a graph-based constituent parsing framework that uses a 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate. |
| Outcome: | The proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. |
Identifying Predictive Causal Factors from News Streams (D19-1)
Copied to clipboard
| Challenge: | Existing word embedding techniques are not suited to learn relationships between words in different documents and contexts. |
| Approach: | They propose a new framework to uncover the relationship between news events and real world phenomena by measuring how word occurrence influences future occurrence. |
| Outcome: | The proposed framework outperforms existing methods in stock price prediction errors for 12 months and 4 years. |
CHIRON: Rich Character Representations in Long-Form Narratives (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems for character representation have simplified the problem of representing complex characters via graphs and brief character descriptions. |
| Approach: | They propose a ‘character sheet’ based representation that organizes and filters textual information about characters. |
| Outcome: | The proposed representation organizes and filters textual information about characters and is better and more flexible than previous models. |
HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models for extractive document summarization are based on sequence-to-sequence (Seq2Sequency) but long-form document summaries using graph-based methods are still an open research issue. |
| Approach: | They propose a heterogeneous graph neural network model to improve the performance of extractive document summarization using graph-based methods. |
| Outcome: | The proposed model can achieve state-of-the-art performance without pre-trained language models. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimoda Emotion Recognition (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing graph-based methods fail to depict global contextual features and local diverse unimodal features in a dialogue. |
| Approach: | They propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition using a multimodal fusion mechanism and a graph contrastative learning framework. |
| Outcome: | The proposed method improves multimodal emotion recognition on unbalanced and small-scale emotional datasets. |
MicroC-KT: Modeling Community Effect via Learning Micro-Environment for Evidence-Grounded Explainable Knowledge Tracing (2026.acl-long)
Copied to clipboard
| Challenge: | Existing graph-based methods focus on exercise-concept relations, but lack the broader context of group references and contrastive evidence. |
| Approach: | They propose a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT by extracting contrastive group evidence. |
| Outcome: | The proposed framework outperforms state-of-the-art models on four public datasets while providing more reliable and evidence-based explanations. |