Machine Reading at the University of Washington

By Hoifung Poon, Janara Christensen, Pedro M. Domingos, Oren Etzioni, Raphael Hoffmann, Chloé Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter and 5 more. 2010

45 citations recorded by the source corpus.

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Bansal
2. Phil Blunsom
3. Claire Cardie
4. Graham Neubig
5. Jianfeng Gao
6. Kyunghyun Cho
7. Mirella Lapata
8. Richard Socher

Citations in the corpus

• Teaching Machines to Read and Comprehend (2015, methodology): “For the Deep LSTM Reader, we consider hidden layer sizes [64, 128, 256], depths [1, 2, 4], initial learning rates [1E 3, 5E 4, 1E 4, 5E 5], batch sizes [16, 32] and dropout [0.”
• Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension (2019, background): “…readers to read and understand a reference material (e.g., a document), and evaluate the comprehension ability of machines by letting them answer questions relevant to the given content (Poon et al., 2010; Richardson et al., 2013), have attracted substantial attention of both academia and industry.”