@inproceedings{abujabal-etal-2017-quint,
title = "{QUINT}: Interpretable Question Answering over Knowledge Bases",
author = "Abujabal, Abdalghani and
Saha Roy, Rishiraj and
Yahya, Mohamed and
Weikum, Gerhard",
editor = "Specia, Lucia and
Post, Matt and
Paul, Michael",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-2011",
doi = "10.18653/v1/D17-2011",
pages = "61--66",
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.",
}
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%0 Conference Proceedings
%T QUINT: Interpretable Question Answering over Knowledge Bases
%A Abujabal, Abdalghani
%A Saha Roy, Rishiraj
%A Yahya, Mohamed
%A Weikum, Gerhard
%Y Specia, Lucia
%Y Post, Matt
%Y Paul, Michael
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F abujabal-etal-2017-quint
%X 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.
%R 10.18653/v1/D17-2011
%U https://aclanthology.org/D17-2011
%U https://doi.org/10.18653/v1/D17-2011
%P 61-66
Markdown (Informal)
[QUINT: Interpretable Question Answering over Knowledge Bases](https://aclanthology.org/D17-2011) (Abujabal et al., EMNLP 2017)
ACL
- Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2017. QUINT: Interpretable Question Answering over Knowledge Bases. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 61–66, Copenhagen, Denmark. Association for Computational Linguistics.