THUDM/CogQA
A multi-hop question answering system that builds cognitive graphs by coordinating BERT-based extraction and graph neural network reasoning.
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CogQA implements a dual-process theory framework for multi-hop reading comprehension at scale. It coordinates an implicit extraction module (System 1) using BERT with an explicit reasoning module (System 2) using graph neural networks to iteratively build cognitive graphs and answer questions. The system retrieves and reasons over Wikipedia documents to provide explainable reasoning paths alongside accurate answers.
Frequently asked
- What is THUDM/CogQA?
- A multi-hop question answering system that builds cognitive graphs by coordinating BERT-based extraction and graph neural network reasoning.
- Is CogQA open source?
- Yes — THUDM/CogQA is open source, released under the MIT license.
- What language is CogQA written in?
- THUDM/CogQA is primarily written in Python.
- How popular is CogQA?
- THUDM/CogQA has 458 stars on GitHub.
- Where can I find CogQA?
- THUDM/CogQA is on GitHub at https://github.com/THUDM/CogQA.