Towards automatically generating Questions under Discussion to link information and discourse structure

Kordula De Kuthy, Madeeswaran Kannan, Haemanth Santhi Ponnusamy, Detmar Meurers


Abstract
Questions under Discussion (QUD; Roberts, 2012) are emerging as a conceptually fruitful approach to spelling out the connection between the information structure of a sentence and the nature of the discourse in which the sentence can function. To make this approach useful for analyzing authentic data, Riester, Brunetti & De Kuthy (2018) presented a discourse annotation framework based on explicit pragmatic principles for determining a QUD for every assertion in a text. De Kuthy et al. (2018) demonstrate that this supports more reliable discourse structure annotation, and Ziai and Meurers (2018) show that based on explicit questions, automatic focus annotation becomes feasible. But both approaches are based on manually specified questions. In this paper, we present an automatic question generation approach to partially automate QUD annotation by generating all potentially relevant questions for a given sentence. While transformation rules can concisely capture the typical question formation process, a rule-based approach is not sufficiently robust for authentic data. We therefore employ the transformation rules to generate a large set of sentence-question-answer triples and train a neural question generation model on them to obtain both systematic question type coverage and robustness.
Anthology ID:
2020.coling-main.509
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5786–5798
Language:
URL:
https://aclanthology.org/2020.coling-main.509
DOI:
10.18653/v1/2020.coling-main.509
Bibkey:
Cite (ACL):
Kordula De Kuthy, Madeeswaran Kannan, Haemanth Santhi Ponnusamy, and Detmar Meurers. 2020. Towards automatically generating Questions under Discussion to link information and discourse structure. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5786–5798, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Towards automatically generating Questions under Discussion to link information and discourse structure (De Kuthy et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.509.pdf