Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation

Subhadarshi Panda, Frank Palma Gomez, Michael Flor, Alla Rozovskaya


Abstract
In a fill-in-the-blank exercise, a student is presented with a carrier sentence with one word hidden, and a multiple-choice list that includes the correct answer and several inappropriate options, called distractors. We propose to automatically generate distractors using round-trip neural machine translation: the carrier sentence is translated from English into another (pivot) language and back, and distractors are produced by aligning the original sentence and its round-trip translation. We show that using hundreds of translations for a given sentence allows us to generate a rich set of challenging distractors. Further, using multiple pivot languages produces a diverse set of candidates. The distractors are evaluated against a real corpus of cloze exercises and checked manually for validity. We demonstrate that the proposed method significantly outperforms two strong baselines.
Anthology ID:
2022.acl-srw.31
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
391–401
Language:
URL:
https://aclanthology.org/2022.acl-srw.31
DOI:
10.18653/v1/2022.acl-srw.31
Bibkey:
Cite (ACL):
Subhadarshi Panda, Frank Palma Gomez, Michael Flor, and Alla Rozovskaya. 2022. Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 391–401, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation (Panda et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-srw.31.pdf