@inproceedings{chen-etal-2020-developing,
title = "Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by {BERT}",
author = "Chen, Tengyang and
Li, Hongyu and
Kasamatsu, Miho and
Utsuro, Takehito and
Kawada, Yasuhide",
editor = "Christodoulopoulos, Christos and
Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Mittal, Arpit",
booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.fever-1.4",
doi = "10.18653/v1/2020.fever-1.4",
pages = "26--35",
abstract = "In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4{\%} of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.",
}
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<abstract>In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4% of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.</abstract>
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%0 Conference Proceedings
%T Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT
%A Chen, Tengyang
%A Li, Hongyu
%A Kasamatsu, Miho
%A Utsuro, Takehito
%A Kawada, Yasuhide
%Y Christodoulopoulos, Christos
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Mittal, Arpit
%S Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-developing
%X In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4% of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.
%R 10.18653/v1/2020.fever-1.4
%U https://aclanthology.org/2020.fever-1.4
%U https://doi.org/10.18653/v1/2020.fever-1.4
%P 26-35
Markdown (Informal)
[Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT](https://aclanthology.org/2020.fever-1.4) (Chen et al., FEVER 2020)
ACL