Papers with message passing
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing approaches to dependency parsing use exact and approximate inference to find the best parse tree. |
| Approach: | They propose a second-order graph-based neural dependency parsing approach using message passing and end-to-end neural networks. |
| Outcome: | The proposed methods match the state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in training and testing. |
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)
Copied to clipboard
| Challenge: | Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. |
| Approach: | They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph . |
| Outcome: | The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime. |
PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods to fix faulty queries are limited in their ability to fix them. |
| Approach: | They propose a Personalized Adaptive Interactions Graph Encoder that integrates user's affinities and query semantics to refine utterance embeddings. |
| Outcome: | The proposed Query Rewriting (QR) techniques improve the rewrite accuracy of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters. |
Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on identifying the causes of emotions by understanding verbal contextual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterrances. |
| Approach: | They propose an Emotional Contagion Graph Network that simulates the impact of non-verbal emotional cues on the counterpart’s emotions. |
| Outcome: | The proposed model is compared with state-of-the-art models on a benchmark dataset and the results are encouraging. |
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies treat ideology as a liberal/conservative binary and fail to capture the spectrum of ideologies that may organically emerge in interconnected online communities. |
| Approach: | They propose a method that uses finetuning language models to probe nuanced ideologies of online communities by analyzing discussions of the 2020 election on Twitter. |
| Outcome: | The proposed approach shows higher alignment than baselines for the proposed approach. |
GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work only encodes entity types and textual context within individual instances, which limits the performance of sentence-level relation extraction (RE). |
| Approach: | They propose a module that aggregates the features from sentences to learn global representations of properties and augments local features within individual sentences. |
| Outcome: | The proposed module can learn global representations of properties from sentences and augment local features within individual sentences. |
Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)
Copied to clipboard
| Challenge: | Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. |
| Approach: | They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. |
| Outcome: | The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models. |
N-ary Relation Extraction using Graph-State LSTM (D18-1)
Copied to clipboard
| Challenge: | Existing methods for cross-sentence relation extraction split the input graph into two DAGs, but important information can be lost in the splitting procedure. |
| Approach: | They propose a graph-state LSTM model which uses a parallel state to model each word, recurrently enriching state values via message passing. |
| Outcome: | The proposed model keeps the original graph structure, and speeds up computation by allowing more parallelization. |
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text. |
| Approach: | They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. |
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing extractive summarization methods focus on balancing salience and redundancy between sentences. |
| Approach: | They propose a hierarchical attentive heterogeneous graph for text summarization that models sentences . they propose to iteratively refine the sentence representations and deliver the labels by message passing . |
| Outcome: | The proposed method outperforms existing extractive summarization methods on large corpus. |
Neural Topic Modeling by Incorporating Document Relationship Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Graph Topic Models (GNNs) capture relationships between graph nodes via message passing . recent research has focused on topic modeling using latent Dirichlet Allocation . |
| Approach: | They propose a Graph Topic Model (GTM) that captures relationships between graph nodes via message passing. |
| Outcome: | The proposed model captures the relationships between nodes via message passing . the results demonstrate that the proposed model is effective in generating documents . |
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted. |
| Approach: | They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage. |
| Outcome: | The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks. |
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Entity and Relation Extraction (ERE) is an important task in information extraction. |
| Approach: | They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module . |
| Outcome: | The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module . |
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing. |
| Approach: | They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. |
| Outcome: | The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures. |
| Approach: | They propose to use graph kernel search to discover subgraphs from the entire graph to bridge the graph and LLMs, helping with graph retrieval and LRM generation. |
| Outcome: | The proposed method achieves state-of-the-art on two graph-based tasks and the results are published in the journal Nature. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |
RouterHGC: Optimized Router for LLM-based Multi-Agent Systems via Heterogeneous Graph Contrastive Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs)-driven Multi-Agent Systems (MAS) have demonstrated remarkable scalability and generalizability across complex tasks. |
| Approach: | They propose a new framework for routing using large language models . they formalize routing as node selection through edge-weight prediction . |
| Outcome: | The proposed framework outperforms the best single LLM and baselines on five datasets . it achieves 0.80%–6.17% accuracy gains on MATH and HotpotQA while reducing inference cost by 27.40%. |
RCTEA: Richness-guided Co-training for Temporal Entity Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing TEA models fail to capture the orthogonal yet complementary effect between structural and temporal features. |
| Approach: | They propose a framework that jointly models structural and temporal aspects of Temporal Knowledge Graphs for entity alignment. |
| Outcome: | The proposed framework achieves state-of-the-art on public TEA benchmarks. |