Semi-supervised training data generation for multilingual question answering

Kyungjae Lee; Kyoungho Yoon; Sunghyun Park; Seung Won Hwang

Semi-supervised training data generation for multilingual question answering

Kyungjae Lee, Kyoungho Yoon, Sunghyun Park, Seung Won Hwang

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

Abstract

Recently, various datasets for question answering (QA) research have been released, such as SQuAD, Marco, WikiQA, MCTest, and SearchQA. However, such existing training resources for these task mostly support only English. In contrast, we study semi-automated creation of the Korean Question Answering Dataset (K-QuAD), by using automatically translated SQuAD and a QA system bootstrapped on a small QA pair set. As a naïve approach for other language, using only machine-translated SQuAD shows limited performance due to translation errors. We study why such approach fails and motivate needs to build seed resources to enable leveraging such resources. Specifically, we annotate seed QA pairs of small size (4K) for Korean language, and design how such seed can be combined with translated English resources. These approach, by combining two resources, leads to 71.50 F1 on Korean QA (comparable to 77.3 F1 on SQuAD).

Original language	English
Title of host publication	LREC 2018 - 11th International Conference on Language Resources and Evaluation
Editors	Hitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga
Publisher	European Language Resources Association (ELRA)
Pages	2758-2762
Number of pages	5
ISBN (Electronic)	9791095546009
Publication status	Published - 2019
Event	11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan Duration: 2018 May 7 → 2018 May 12

Publication series

Name	LREC 2018 - 11th International Conference on Language Resources and Evaluation

Other

Other	11th International Conference on Language Resources and Evaluation, LREC 2018
Country/Territory	Japan
City	Miyazaki
Period	18/5/7 → 18/5/12

Bibliographical note

Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2016-0-00464-002) supervised by the IITP(Institute for Information communications Technology Promotion)

Publisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.

All Science Journal Classification (ASJC) codes

Linguistics and Language
Education
Library and Information Sciences
Language and Linguistics

Cite this

Lee, K., Yoon, K., Park, S., & Hwang, S. W. (2019). Semi-supervised training data generation for multilingual question answering. In H. Isahara, B. Maegaard, S. Piperidis, C. Cieri, T. Declerck, K. Hasida, H. Mazo, K. Choukri, S. Goggi, J. Mariani, A. Moreno, N. Calzolari, J. Odijk, & T. Tokunaga (Eds.), LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 2758-2762). (LREC 2018 - 11th International Conference on Language Resources and Evaluation). European Language Resources Association (ELRA).

Lee, Kyungjae ; Yoon, Kyoungho ; Park, Sunghyun et al. / Semi-supervised training data generation for multilingual question answering. LREC 2018 - 11th International Conference on Language Resources and Evaluation. editor / Hitoshi Isahara ; Bente Maegaard ; Stelios Piperidis ; Christopher Cieri ; Thierry Declerck ; Koiti Hasida ; Helene Mazo ; Khalid Choukri ; Sara Goggi ; Joseph Mariani ; Asuncion Moreno ; Nicoletta Calzolari ; Jan Odijk ; Takenobu Tokunaga. European Language Resources Association (ELRA), 2019. pp. 2758-2762 (LREC 2018 - 11th International Conference on Language Resources and Evaluation).

@inproceedings{8a8054a8ac47429eb1cde3badea9a7c6,

title = "Semi-supervised training data generation for multilingual question answering",

abstract = "Recently, various datasets for question answering (QA) research have been released, such as SQuAD, Marco, WikiQA, MCTest, and SearchQA. However, such existing training resources for these task mostly support only English. In contrast, we study semi-automated creation of the Korean Question Answering Dataset (K-QuAD), by using automatically translated SQuAD and a QA system bootstrapped on a small QA pair set. As a na{\"i}ve approach for other language, using only machine-translated SQuAD shows limited performance due to translation errors. We study why such approach fails and motivate needs to build seed resources to enable leveraging such resources. Specifically, we annotate seed QA pairs of small size (4K) for Korean language, and design how such seed can be combined with translated English resources. These approach, by combining two resources, leads to 71.50 F1 on Korean QA (comparable to 77.3 F1 on SQuAD).",

author = "Kyungjae Lee and Kyoungho Yoon and Sunghyun Park and Hwang, {Seung Won}",

note = "Funding Information: This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2016-0-00464-002) supervised by the IITP(Institute for Information communications Technology Promotion) Publisher Copyright: {\textcopyright} LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.; 11th International Conference on Language Resources and Evaluation, LREC 2018 ; Conference date: 07-05-2018 Through 12-05-2018",

year = "2019",

language = "English",

series = "LREC 2018 - 11th International Conference on Language Resources and Evaluation",

publisher = "European Language Resources Association (ELRA)",

pages = "2758--2762",

editor = "Hitoshi Isahara and Bente Maegaard and Stelios Piperidis and Christopher Cieri and Thierry Declerck and Koiti Hasida and Helene Mazo and Khalid Choukri and Sara Goggi and Joseph Mariani and Asuncion Moreno and Nicoletta Calzolari and Jan Odijk and Takenobu Tokunaga",

booktitle = "LREC 2018 - 11th International Conference on Language Resources and Evaluation",

}

Lee, K, Yoon, K, Park, S & Hwang, SW 2019, Semi-supervised training data generation for multilingual question answering. in H Isahara, B Maegaard, S Piperidis, C Cieri, T Declerck, K Hasida, H Mazo, K Choukri, S Goggi, J Mariani, A Moreno, N Calzolari, J Odijk & T Tokunaga (eds), LREC 2018 - 11th International Conference on Language Resources and Evaluation. LREC 2018 - 11th International Conference on Language Resources and Evaluation, European Language Resources Association (ELRA), pp. 2758-2762, 11th International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, 18/5/7.

Semi-supervised training data generation for multilingual question answering. / Lee, Kyungjae; Yoon, Kyoungho; Park, Sunghyun et al.

LREC 2018 - 11th International Conference on Language Resources and Evaluation. ed. / Hitoshi Isahara; Bente Maegaard; Stelios Piperidis; Christopher Cieri; Thierry Declerck; Koiti Hasida; Helene Mazo; Khalid Choukri; Sara Goggi; Joseph Mariani; Asuncion Moreno; Nicoletta Calzolari; Jan Odijk; Takenobu Tokunaga. European Language Resources Association (ELRA), 2019. p. 2758-2762 (LREC 2018 - 11th International Conference on Language Resources and Evaluation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Semi-supervised training data generation for multilingual question answering

AU - Lee, Kyungjae

AU - Yoon, Kyoungho

AU - Park, Sunghyun

AU - Hwang, Seung Won

N1 - Funding Information: This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2016-0-00464-002) supervised by the IITP(Institute for Information communications Technology Promotion) Publisher Copyright: © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.

PY - 2019

Y1 - 2019

N2 - Recently, various datasets for question answering (QA) research have been released, such as SQuAD, Marco, WikiQA, MCTest, and SearchQA. However, such existing training resources for these task mostly support only English. In contrast, we study semi-automated creation of the Korean Question Answering Dataset (K-QuAD), by using automatically translated SQuAD and a QA system bootstrapped on a small QA pair set. As a naïve approach for other language, using only machine-translated SQuAD shows limited performance due to translation errors. We study why such approach fails and motivate needs to build seed resources to enable leveraging such resources. Specifically, we annotate seed QA pairs of small size (4K) for Korean language, and design how such seed can be combined with translated English resources. These approach, by combining two resources, leads to 71.50 F1 on Korean QA (comparable to 77.3 F1 on SQuAD).

AB - Recently, various datasets for question answering (QA) research have been released, such as SQuAD, Marco, WikiQA, MCTest, and SearchQA. However, such existing training resources for these task mostly support only English. In contrast, we study semi-automated creation of the Korean Question Answering Dataset (K-QuAD), by using automatically translated SQuAD and a QA system bootstrapped on a small QA pair set. As a naïve approach for other language, using only machine-translated SQuAD shows limited performance due to translation errors. We study why such approach fails and motivate needs to build seed resources to enable leveraging such resources. Specifically, we annotate seed QA pairs of small size (4K) for Korean language, and design how such seed can be combined with translated English resources. These approach, by combining two resources, leads to 71.50 F1 on Korean QA (comparable to 77.3 F1 on SQuAD).

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M3 - Conference contribution

AN - SCOPUS:85059916026

T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation

SP - 2758

EP - 2762

BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation

A2 - Isahara, Hitoshi

A2 - Maegaard, Bente

A2 - Piperidis, Stelios

A2 - Cieri, Christopher

A2 - Declerck, Thierry

A2 - Hasida, Koiti

A2 - Mazo, Helene

A2 - Choukri, Khalid

A2 - Goggi, Sara

A2 - Mariani, Joseph

A2 - Moreno, Asuncion

A2 - Calzolari, Nicoletta

A2 - Odijk, Jan

A2 - Tokunaga, Takenobu

PB - European Language Resources Association (ELRA)

T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018

Y2 - 7 May 2018 through 12 May 2018

ER -

Lee K, Yoon K, Park S, Hwang SW. Semi-supervised training data generation for multilingual question answering. In Isahara H, Maegaard B, Piperidis S, Cieri C, Declerck T, Hasida K, Mazo H, Choukri K, Goggi S, Mariani J, Moreno A, Calzolari N, Odijk J, Tokunaga T, editors, LREC 2018 - 11th International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA). 2019. p. 2758-2762. (LREC 2018 - 11th International Conference on Language Resources and Evaluation).

Semi-supervised training data generation for multilingual question answering

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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