Papers with Wikipedia info-boxes
Simple Large-scale Relation Extraction from Unstructured Text (L18-1)
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| Challenge: | Knowledge-based question answering relies on the availability of facts, most of which cannot be found in structured sources. |
| Approach: | They propose a method for creating distant (weak) supervision labels for training a large-scale RE system by decoupling the model architecture from the feature design of a state-of-the-art neural network system. |
| Outcome: | The proposed method performs on par with the state-of-the-art model with similar features at 75x reduction in training time. |
INFOTABS: Inference on Tables as Semi-structured Data (2020.acl-main)
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| Challenge: | Existing models for text understanding lack human-parity across a wide array of reasoning skills. |
| Approach: | They propose an extension of the natural language inference task to include semi-structured tabulated text . they propose a semi-structural, multi-domain and heterogeneous nature of the premises that are tables extracted from Wikipedia info-boxes. |
| Outcome: | The proposed model outperforms baseline models on the GLUE benchmark suite. |