Semi-supervised training data generation for multilingual question answering

Kyungjae Lee, Kyoungho Yoon, Sunghyun Park, Seungwon Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationLREC 2018 - 11th International Conference on Language Resources and Evaluation
EditorsHitoshi 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
PublisherEuropean Language Resources Association (ELRA)
Pages2758-2762
Number of pages5
ISBN (Electronic)9791095546009
Publication statusPublished - 2019 Jan 1
Event11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan
Duration: 2018 May 72018 May 12

Other

Other11th International Conference on Language Resources and Evaluation, LREC 2018
CountryJapan
CityMiyazaki
Period18/5/718/5/12

Fingerprint

resources
language
Question Answering
Resources
performance
Korean Language
Language
Nave
English-only

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. (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). European Language Resources Association (ELRA).
Lee, Kyungjae ; Yoon, Kyoungho ; Park, Sunghyun ; Hwang, Seungwon. / 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
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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).",
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Lee, K, Yoon, K, Park, S & Hwang, S 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. 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; Hwang, Seungwon.

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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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

AU - Lee, Kyungjae

AU - Yoon, Kyoungho

AU - Park, Sunghyun

AU - Hwang, Seungwon

PY - 2019/1/1

Y1 - 2019/1/1

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

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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)

ER -

Lee K, Yoon K, Park S, Hwang S. 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