Meta Answering for Machine Reading

Benjamin Borschinger; Jordan Boyd-Graber; Christian Buck; Jannis Bulian; Massimiliano Ciaramita; Michelle Chen Huebscher; Wojciech Gajewski; Yannic Kilcher; Rodrigo Nogueira; Lierni Sestorain Saralegu

arXiv · 1911.04156

Meta Answering for Machine Reading

Benjamin Borschinger·Jordan Boyd-Graber·Christian Buck·Jannis Bulian·Massimiliano Ciaramita·Michelle Chen Huebscher·Wojciech Gajewski·Yannic Kilcher·Rodrigo Nogueira·Lierni Sestorain Saralegu

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Abstract

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.

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BibTeX RIS

Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, Lierni Sestorain Saralegu. 2020-04-30. Meta Answering for Machine Reading. https://arxiv.org/abs/1911.04156

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