IEICE TRANSACTIONS on Information
Classification of Utterances Based on Multiple BLEU Scores for Translation-Game-Type CALL Systems
Summary :
This paper proposes a classification method of second-language-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POS-replacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.3 pp.750-757
- Publication Date
- 2018/03/01
- Publicized
- 2017/12/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7151
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
Authors
Reiko KUWA
Doshisha University
Tsuneo KATO
Doshisha University
Seiichi YAMAMOTO
Doshisha University
Keyword
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Reiko KUWA, Tsuneo KATO, Seiichi YAMAMOTO, "Classification of Utterances Based on Multiple BLEU Scores for Translation-Game-Type CALL Systems" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 3, pp. 750-757, March 2018, doi: 10.1587/transinf.2017EDP7151.
Abstract: This paper proposes a classification method of second-language-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POS-replacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7151/_p
Copy
@ARTICLE{e101-d_3_750,
author={Reiko KUWA, Tsuneo KATO, Seiichi YAMAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Classification of Utterances Based on Multiple BLEU Scores for Translation-Game-Type CALL Systems},
year={2018},
volume={E101-D},
number={3},
pages={750-757},
abstract={This paper proposes a classification method of second-language-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POS-replacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.},
keywords={},
doi={10.1587/transinf.2017EDP7151},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Classification of Utterances Based on Multiple BLEU Scores for Translation-Game-Type CALL Systems
T2 - IEICE TRANSACTIONS on Information
SP - 750
EP - 757
AU - Reiko KUWA
AU - Tsuneo KATO
AU - Seiichi YAMAMOTO
PY - 2018
DO - 10.1587/transinf.2017EDP7151
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2018
AB - This paper proposes a classification method of second-language-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POS-replacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.
ER -
English
