Analysis of Wikipedia-based Corpora for Question Answering
By Tomasz Jurczyk, Amit Ajit Deshmane, Jinho D. Choi. 2018
6 citations recorded by the source corpus.
This paper gives comprehensive analyses of corpora based on Wikipedia for several tasks in question answering. Four recent corpora are collected,WikiQA, SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual similarities, question types, and answer categories. These corpora are then analyzed extrinsically by three question answering tasks, answer retrieval, selection, and triggering. An indexing-based method for the creation of a silver-standard dataset for answer retrieval using the entire Wikipedia is also presented. Our analysis shows the uniqueness of these corpora and suggests a better use of them for statistical question answering learning.
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
- Luke S. Zettlemoyer
- Mohit Bansal
- Yejin Choi
- Percy Liang
- Hannaneh Hajishirzi
- Jianfeng Gao
- Wen-tau Yih
- Noah A. Smith
In Natural Language Processing
- Yejin Choi
- Luke S. Zettlemoyer
- Percy Liang
- Mohit Bansal
- Wen-tau Yih
- Jonathan Berant
- Hannaneh Hajishirzi
- Eunsol Choi
Citations in the corpus
- QuAC: Question Answering in Context (2018, background): “SQuAD, which is unsurprising as most SQuAD answers are either entities or numerics (Jurczyk et al., 2018) while questions can be more open-ended.”