Translating Questions into Answers using DBPedia n-triples
Mihael Arcan

TL;DR
This paper introduces a neural network-based question answering system that interprets questions using DBpedia n-triples and movie subtitle dialogues, showing promising results despite low automatic evaluation overlap.
Contribution
It presents a novel approach combining DBpedia n-triples and movie dialogues for training a question answering neural network.
Findings
Manual inspection shows promising answer quality
Low overlap in automatic evaluation suggests room for improvement
Combines structured data with natural language dialogues
Abstract
In this paper we present a question answering system using a neural network to interpret questions learned from the DBpedia repository. We train a sequence-to-sequence neural network model with n-triples extracted from the DBpedia Infobox Properties. Since these properties do not represent the natural language, we further used question-answer dialogues from movie subtitles. Although the automatic evaluation shows a low overlap of the generated answers compared to the gold standard set, a manual inspection of the showed promising outcomes from the experiment for further work.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
