Empirical Study on Deep Learning Models for QA

By Yang Yu, Chung-Wei Hang, Bowen Zhou. 2015

5 citations recorded by the source corpus.

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Jie Zhou
2. Peng Li
3. Wei Xu
4. Ying Cao
5. Xuguang Wang
6. Quoc V. Le
7. Luke S. Zettlemoyer
8. Devi Parikh

In Natural Language Processing

1. Jie Zhou
2. Peng Li
3. Ying Cao
4. Xuguang Wang
5. Wei Xu
6. Luke S. Zettlemoyer
7. Quoc V. Le
8. Bishan Yang

Citations in the corpus

• Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation (2016, background): “Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question answering systems (Yu et al., 2015) and image caption generation (Mao et al., 2014).”
• End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension (2016, neutral): “…neural networks like Neural Turing Machines (Graves, Wayne, and Danihelka 2014) and its variants (Zhang, Yu, and Zhou 2015; Gulcehre et al. 2016; Zaremba and Sutskever 2015) were also potential candidates for the task, and Gulcehre et al. (2016) reported results on the bAbI task, which is worse…”