A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation

Jan Deriu, Katsiaryna Mlynchyk, Philippe Schläpfer, Alvaro Rodrigo, Dirk von Grünigen, Nicolas Kaiser, Kurt Stockinger, Eneko Agirre, Mark Cieliebak


Abstract
In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database, called Operation Trees (OT). This representation allows us to invert the annotation process without loosing flexibility in the types of queries that we generate. Furthermore, it allows for fine-grained alignment of the tokens to the operations. Thus, we randomly generate OTs from a context free grammar and annotators just have to write the appropriate question and assign the tokens. We compare our corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases, to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries. Finally, we train a state-of-the-art semantic parsing model on our data and show that our dataset is a challenging dataset and that the token alignment can be leveraged to significantly increase the performance.
Anthology ID:
2020.acl-main.84
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
897–911
Language:
URL:
https://aclanthology.org/2020.acl-main.84
DOI:
10.18653/v1/2020.acl-main.84
Bibkey:
Cite (ACL):
Jan Deriu, Katsiaryna Mlynchyk, Philippe Schläpfer, Alvaro Rodrigo, Dirk von Grünigen, Nicolas Kaiser, Kurt Stockinger, Eneko Agirre, and Mark Cieliebak. 2020. A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 897–911, Online. Association for Computational Linguistics.
Cite (Informal):
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation (Deriu et al., ACL 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.acl-main.84.pdf
Dataset:
 2020.acl-main.84.Dataset.zip
Video:
 http://slideslive.com/38929033