QUINT: Interpretable Question Answering over Knowledge Bases

Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum


Abstract
We 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.
Anthology ID:
D17-2011
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–66
Language:
URL:
https://www.aclweb.org/anthology/D17-2011
DOI:
10.18653/v1/D17-2011
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-2011.pdf