Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment
Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2078279 |
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| _version_ | 1850169146929577984 |
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| author | Xinguang Li Xiaoning Li Shuai Chen Shanxian Ma Fenfang Xie |
| author_facet | Xinguang Li Xiaoning Li Shuai Chen Shanxian Ma Fenfang Xie |
| author_sort | Xinguang Li |
| collection | DOAJ |
| description | Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring. |
| format | Article |
| id | doaj-art-a643012d2d3c46e8ba9439d08f8733fe |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-a643012d2d3c46e8ba9439d08f8733fe2025-08-20T02:20:48ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411638165310.1080/09540091.2022.20782792078279Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessmentXinguang Li0Xiaoning Li1Shuai Chen2Shanxian Ma3Fenfang Xie4Guangdong University of Foreign StudiesGuangdong University of Foreign StudiesGuangdong University of Foreign StudiesGuangdong University of Foreign StudiesGuangdong University of Foreign StudiesManual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring.http://dx.doi.org/10.1080/09540091.2022.2078279automatic scoring modelchinese-english interpretationmulti-indicator assessmentattention mechanismbilstm |
| spellingShingle | Xinguang Li Xiaoning Li Shuai Chen Shanxian Ma Fenfang Xie Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment Connection Science automatic scoring model chinese-english interpretation multi-indicator assessment attention mechanism bilstm |
| title | Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment |
| title_full | Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment |
| title_fullStr | Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment |
| title_full_unstemmed | Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment |
| title_short | Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment |
| title_sort | neural based automatic scoring model for chinese english interpretation with a multi indicator assessment |
| topic | automatic scoring model chinese-english interpretation multi-indicator assessment attention mechanism bilstm |
| url | http://dx.doi.org/10.1080/09540091.2022.2078279 |
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