A Machine Learning Approach to Answering Questions for Reading Comprehension Tests

By Hwee Tou Ng, Leonghwee Teo, Jennifer Lai-Pheng Kwan. 2000

60 citations recorded by the source corpus.

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We evaluated our approach on the Remedia data set, a common data set used in several recent papers on the reading comprehension task. Our learning approach achieves accuracy competitive to previous approaches that rely on hand-crafted, deterministic rules and algorithms. To the best of our knowledge, this is the first work that reports that the use of a machine learning approach achieves competitive results on answering questions for reading comprehension tests.

Subfields: Artificial Intelligence, Natural Language Processing, 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. Jianfeng Gao
2. Li Deng
3. Ellen Riloff
4. C. Burges
5. Geoffry Zweig
6. Percy Liang
7. Qiang Liu
8. Edward A. Fox

In Natural Language Processing

1. Michael Thelen
2. Geoffry Zweig
3. C. Burges
4. Ellen Riloff
5. Christopher Meek
6. Edward A. Fox
7. Nader Tavvaf
8. Yaser Keneshloo

In Question Answering & Reading Comprehension

1. C. Burges
2. Sanda M. Harabagiu
3. Marius Pasca
4. Steven J. Maiorano
5. Christopher Meek

Citations in the corpus

• Open-domain textual question answering techniques (2003, methodology): “The same corpus was later used by several other RC Q/A systems such as Quarc (Riloff and Thelen 2000), Aquareas (Ng, Teo and Kwan 2000) and the Brown University statistical language processing class project (Charniak, Altun, de Salvo Braz, Garrett, Kosmala, Moscovich, Pang, Pyo, Sun, Wy, Zang,…”
• Looking Under the Hood: Tools for Diagnosing Your Question Answering Engine (2001, background): “…systems to take reading comprehension examinations designed to evaluate children’s reading levels (Charniak et al., 2000; Hirschman et al., 1999; Ng et al., 2000; 1This paper contains a revised Table 2 replacing the one appearing in the Proceedings of the Workshop on OpenDomain Question…”
• Computational Approaches to Sentence Completion (2012, background): “, 1999) initiated a long line of research into reading comprehension based on test prep material (Charniak et al., 2000; Riloff and Thelen, 2000; Wang et al., 2000; Ng et al., 2000).”
• Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “The main contribution of [146] is proposing a feature vector representation framework which is aimed to provide information for learning the model.”