A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension

By Seunghak Yu, Sathish Reddy Indurthi, Seohyun Back, Haejun Lee. 2018

23 citations recorded by the source corpus.

Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.

Subfields: Artificial Intelligence, Natural Language Processing, Model Architectures, Question Answering & Reading Comprehension

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. Shuming Ma
2. Zhoujun Li
3. Shuangzhi Wu
4. Jian Yang
5. Dongdong Zhang
6. Ming Zhou
7. Furu Wei
8. Jianfeng Gao

In Natural Language Processing

1. Zhoujun Li
2. Shuming Ma
3. Shuangzhi Wu
4. Jian Yang
5. Dongdong Zhang
6. Ming Zhou
7. Jason Aaron Edward Weston

In Model Architectures

1. Zhoujun Li
2. Shuming Ma
3. Shuangzhi Wu
4. Dongdong Zhang
5. Ming Zhou

Citations in the corpus

• The Effect of Natural Distribution Shift on Question Answering Models (2020, neutral): “MAMCN (Single) (Yu et al., 2018) https://worksheets.”
• Alternating Language Modeling for Cross-Lingual Pre-Training (2020, background): “…et al. 2019), have achieved impressive results on various natural language processing tasks such as question-answering (Min, Seo, and Hajishirzi 2017; Yang et al. 2019a), machine reading comprehension (Salant and Berant 2018; Yu et al. 2018) and natural language inference (Tay, Luu, and Hui 2018).”