@inproceedings{panda-etal-2022-automatic,
title = "Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation",
author = "Panda, Subhadarshi and
Palma Gomez, Frank and
Flor, Michael and
Rozovskaya, Alla",
editor = "Louvan, Samuel and
Madotto, Andrea and
Madureira, Brielen",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.31",
doi = "10.18653/v1/2022.acl-srw.31",
pages = "391--401",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation
%A Panda, Subhadarshi
%A Palma Gomez, Frank
%A Flor, Michael
%A Rozovskaya, Alla
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F panda-etal-2022-automatic
%X 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.
%R 10.18653/v1/2022.acl-srw.31
%U https://aclanthology.org/2022.acl-srw.31
%U https://doi.org/10.18653/v1/2022.acl-srw.31
%P 391-401
Markdown (Informal)
[Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation](https://aclanthology.org/2022.acl-srw.31) (Panda et al., ACL 2022)
ACL