Graph Reasoning Transformers for Knowledge-Aware Question Answering
- Publisher:
- Association for the Advancement of Artificial Intelligence (AAAI)
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38, (17), pp. 19652-19660
- Issue Date:
- 2024-03-25
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Graph Reasoning Transformers.pdf | Published version | 296.06 kB |
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Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete knowledge-intensive tasks. However, existing methods are unable to effectively utilize the structured knowledge in a KG due to their inability to capture the rich relational semantics of knowledge triplets. Moreover, the modality gap between natural language text and KGs has become a challenging obstacle when aligning and fusing cross-modal information. To address these challenges, we propose a novel knowledge-augmented question answering (QA) model, namely, Graph Reasoning Transformers (GRT). Different from conventional node-level methods, the GRT serves knowledge triplets as atomic knowledge and utilize a triplet-level graph encoder to capture triplet-level graph features. Furthermore, to alleviate the negative effect of the modality gap on joint reasoning, we propose a representation alignment pretraining to align the cross-modal representations and introduce a cross-modal information fusion module with attention bias to enable cross-modal information fusion. Extensive experiments conducted on three knowledge-intensive QA benchmarks show that the GRT outperforms the state-of-the-art KG-augmented QA systems, demonstrating the effectiveness and adaptation of our proposed model.
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