| Autor: |
Borschinger, Benjamin, Boyd-Graber, Jordan, Buck, Christian, Bulian, Jannis, Ciaramita, Massimiliano, Huebscher, Michelle Chen, Gajewski, Wojciech, Kilcher, Yannic, Nogueira, Rodrigo, Saralegu, Lierni Sestorain |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BERT, providing candidate answers to questions, and possibly some context. To validate the realism of our formulation, we ask humans to play the role of a meta-answerer. With just a small snippet of text around an answer, humans can outperform the machine reader, improving recall. Similarly, a simple machine meta-answerer outperforms the environment, improving both precision and recall on the Natural Questions dataset. The system relies on joint training of answer scoring and the selection of conditioning information. |
| Databáze: |
arXiv |
| Externí odkaz: |
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