Papers with graph models
Dialogue Graph Modeling for Conversational Machine Reading (2021.findings-acl)
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| Challenge: | Existing methods for conversational machine reading (CMR) are not effective for capturing multiple objects in complex interactive scenarios. |
| Approach: | They propose a dialogue graph modeling framework that captures explicit and implicit interactions hidden in the rule documents and a model that asks clarification questions to the machine. |
| Outcome: | The proposed model exceeds the milestone accuracy score of 80% on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone precision score of 90%. |
Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)
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| Challenge: | Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks. |
| Approach: | They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. |
| Outcome: | The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective. |
Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks (2024.lrec-main)
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| Challenge: | Event Coreference Resolution is a discourse-oriented task that requires a lot of computational power. |
| Approach: | They propose a method to combine traditional mention-pair coreference models with a graph reconstruction algorithm. |
| Outcome: | The proposed method is highly robust in low-data settings and scales with increases in performance for the underlying mention-pair models. |
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved . |
| Approach: | They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values. |
| Outcome: | The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training. |
UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language (2023.acl-long)
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| Challenge: | Existing studies have incorporated contextual information to better learn the representation of political actors for specific tasks. |
| Approach: | They propose to use statements to represent political actors and learn mapping from languages to representations using social networks and behaviors as self-constructed supervision. |
| Outcome: | The proposed model can be generalized to political actors and solve downstream tasks. |
PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)
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| Challenge: | Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information. |
| Approach: | They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model. |
| Outcome: | The proposed framework can represent users based on text even without social network information on microblogs. |
Quantifying Compositionality of Classic and State-of-the-Art Embeddings (2025.findings-emnlp)
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| Challenge: | Static word embeddings make strong claims about compositionality, but the SOTA generative models go too far in the other direction. |
| Approach: | a new study evaluates the compositionality of word embeddings by canonical correlation analysis . strong compositional signals are observed in later training stages across data modalities . |
| Outcome: | a new evaluation of compositional models shows that they exploit access meanings when justified . strong compositional signals are observed in later training stages and in deeper layers of the transformer-based model before a decline at the top layer. |
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders (2024.lrec-main)
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| Challenge: | Existing approaches to intent prediction are limited in highly specialized fields, such as closed-domain dialogue systems, where context comprehension is of paramount importance. |
| Approach: | They propose a method that uses scenario dialog graphs to model dialogues as sequences of transitions between intents, representing distinct goals or requests. |
| Outcome: | The proposed method significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches. |
Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)
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| Challenge: | Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment. |
| Approach: | They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
| Outcome: | The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |