2017
EMNLP
EMNLP 2017
QUINT: Interpretable Question Answering over Knowledge Bases
Abstract
AbstractWe present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.
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Interdisciplinary Bridge
— Artificial Intelligence and Knowledge & Reasoning and Natural Language Processing
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Trend Setter
— Question Answering
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Question Answering
Knowledge & Reasoning > Representation > Knowledge Graphs
Artificial Intelligence > Core AI > Knowledge Graph
Artificial Intelligence > Core AI > Question Answering