@inproceedings{lovenia-etal-2022-clozer,
title = "Clozer{''}:{''} Adaptable Data Augmentation for Cloze-style Reading Comprehension",
author = "Lovenia, Holy and
Wilie, Bryan and
Chung, Willy and
Min, Zeng and
Cahyawijaya, Samuel and
Su, Dan and
Fung, Pascale",
editor = "Gella, Spandana and
He, He and
Majumder, Bodhisattwa Prasad and
Can, Burcu and
Giunchiglia, Eleonora and
Cahyawijaya, Samuel and
Min, Sewon and
Mozes, Maximilian and
Li, Xiang Lorraine and
Augenstein, Isabelle and
Rogers, Anna and
Cho, Kyunghyun and
Grefenstette, Edward and
Rimell, Laura and
Dyer, Chris",
booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.repl4nlp-1.7",
doi = "10.18653/v1/2022.repl4nlp-1.7",
pages = "60--66",
abstract = "Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules that cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze answer extraction method used in TAPT that is extendable for adaptation on any cloze-style machine reading comprehension (MRC) downstream tasks. We experiment on multiple-choice cloze-style MRC tasks, and show that Clozer performs significantly better compared to the oracle and state-of-the-art in escalating TAPT effectiveness in lifting model performance, and prove that Clozer is able to recognize the gold answers independently of any heuristics.",
}
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<abstract>Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules that cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze answer extraction method used in TAPT that is extendable for adaptation on any cloze-style machine reading comprehension (MRC) downstream tasks. We experiment on multiple-choice cloze-style MRC tasks, and show that Clozer performs significantly better compared to the oracle and state-of-the-art in escalating TAPT effectiveness in lifting model performance, and prove that Clozer is able to recognize the gold answers independently of any heuristics.</abstract>
<identifier type="citekey">lovenia-etal-2022-clozer</identifier>
<identifier type="doi">10.18653/v1/2022.repl4nlp-1.7</identifier>
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<url>https://aclanthology.org/2022.repl4nlp-1.7</url>
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<date>2022-05</date>
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%0 Conference Proceedings
%T Clozer”:” Adaptable Data Augmentation for Cloze-style Reading Comprehension
%A Lovenia, Holy
%A Wilie, Bryan
%A Chung, Willy
%A Min, Zeng
%A Cahyawijaya, Samuel
%A Su, Dan
%A Fung, Pascale
%Y Gella, Spandana
%Y He, He
%Y Majumder, Bodhisattwa Prasad
%Y Can, Burcu
%Y Giunchiglia, Eleonora
%Y Cahyawijaya, Samuel
%Y Min, Sewon
%Y Mozes, Maximilian
%Y Li, Xiang Lorraine
%Y Augenstein, Isabelle
%Y Rogers, Anna
%Y Cho, Kyunghyun
%Y Grefenstette, Edward
%Y Rimell, Laura
%Y Dyer, Chris
%S Proceedings of the 7th Workshop on Representation Learning for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lovenia-etal-2022-clozer
%X Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules that cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze answer extraction method used in TAPT that is extendable for adaptation on any cloze-style machine reading comprehension (MRC) downstream tasks. We experiment on multiple-choice cloze-style MRC tasks, and show that Clozer performs significantly better compared to the oracle and state-of-the-art in escalating TAPT effectiveness in lifting model performance, and prove that Clozer is able to recognize the gold answers independently of any heuristics.
%R 10.18653/v1/2022.repl4nlp-1.7
%U https://aclanthology.org/2022.repl4nlp-1.7
%U https://doi.org/10.18653/v1/2022.repl4nlp-1.7
%P 60-66
Markdown (Informal)
[Clozer”:” Adaptable Data Augmentation for Cloze-style Reading Comprehension](https://aclanthology.org/2022.repl4nlp-1.7) (Lovenia et al., RepL4NLP 2022)
ACL