Learning to Answer Questions from Wikipedia Infoboxes

By Alvaro Morales, Varot Premtoon, Cordelia Avery, Sue Felshin, Boris Katz. 2016

19 citations recorded by the source corpus.

A natural language interface to answers on the Web can help us access information more ef-ﬁciently. We start with an interesting source of information—infoboxes in Wikipedia that summarize factoid knowledge—and develop a comprehensive approach to answering questions with high precision. We ﬁrst build a system to access data in infoboxes in a structured manner. We use our system to construct a crowdsourced dataset of over 15,000 high-quality, diverse questions. With these questions, we train a convolutional neural network model that outperforms models that achieve top results in similar answer selection tasks.

Subfields: Artificial Intelligence, Communication, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Zero-Shot Relation Extraction via Reading Comprehension (2017, methodology): “Following the methodology of InfoboxQA (Morales et al., 2016), we generate negative examples by matching (for the same entity e ) a question q that pertains to one relation with a sentence s that expresses another relation.”
Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, methodology): “W IKIPEDIA has thus been used for a wealth of research to build datasets posing queries about a single sentence (Morales et al., 2016; Levy et al., 2017) or article (Yang et al., 2015a; Hewlett et al., 2016; Ra-jpurkar et al., 2016).”
Analysis of Wikipedia-based Corpora for Question Answering (2018, background): “Finally, Morales et al. (2016) provided INFOBOXQA for answer selection.”