Papers with Graph Attention Networks
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks (2021.eacl-main)
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| Challenge: | Existing knowledge graphs are widely used for (complex) conversational question answering . LASAGNE improves the F1-score on eight out of ten question types . |
| Approach: | They propose a multi-task neural semantic parsing approach for (complex) conversational question answering over a knowledge graph using a transformer model and a Graph Attention Networks model. |
| Outcome: | The proposed approach outperforms baselines on eight out of ten question types on a standard dataset for complex sequential question answering. |
Incorporating Global Information in Local Attention for Knowledge Representation Learning (2021.findings-acl)
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| Challenge: | Graph Attention Networks (GATs) are a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data. |
| Approach: | They propose to incorporate global information into the GAT family of models by using an attention-based global random walk algorithm. |
| Outcome: | Experimental results on KG entity prediction against the state-of-the-arts demonstrate the effectiveness of the proposed model. |
Towards Knowledge-Augmented Visual Question Answering (2020.coling-main)
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| Challenge: | Visual Question Answering (VQA) is a challenging task for humans, but it is effortless for visual-based approaches. |
| Approach: | They propose a visual-based approach that captures interactions between visual scenes and external knowledge sources and exploits ConceptNet as the source of general knowledge. |
| Outcome: | The proposed model learns a question-adaptive graph representation of related knowledge instances. |
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)
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| Challenge: | Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations. |
| Approach: | They propose a model that captures the sociological phenomenon of homophily and combines it with linguistic information to make a prediction. |
| Outcome: | The proposed model significantly outperforms existing models on three different tasks and is compared with other 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. |
Fine-grained Fact Verification with Kernel Graph Attention Network (2020.acl-main)
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| Challenge: | Existing methods for fact verification are based on dot-product attentions, but kernel-based attentions focus more on relevant evidence sentences and meaningful clues in the evidence graph. |
| Approach: | They propose a kernel-based attention network which conducts more fine-grained fact verification with kernel-basic attentions. |
| Outcome: | The proposed task achieves a 70.38% FEVER score and significantly outperforms existing fact verification models on FEVER, a large-scale benchmark for fact verification. |