This is pretty impressive. It beats average human performance in the history category, but is outperformed by humans in literature.
Note that part of the data preparation process includes building a dependency-parse tree. From the abstract I'd thought the model was learning to do that too, which would have been very impressive.
In general this approach is somewhat related to [1] in that both rely on knowledge representation similarities. I'm not entirely clear about how this groups approach interfaces the learned model and the IR querying.
Comments
This is pretty impressive. It beats average human performance in the history category, but is outperformed by humans in literature.
Note that part of the data preparation process includes building a dependency-parse tree. From the abstract I'd thought the model was learning to do that too, which would have been very impressive.
In general this approach is somewhat related to [1] in that both rely on knowledge representation similarities. I'm not entirely clear about how this groups approach interfaces the learned model and the IR querying.
[1] Open Question Answering with Weakly Supervised Embedding Models http://arxiv.org/pdf/1404.4326v1.pdf