| 研究課題/領域番号 |
22K00792
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分02100:外国語教育関連
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| 研究機関 | 会津大学 |
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研究代表者 |
BLAKE John 会津大学, コンピュータ理工学部, 上級准教授 (80635954)
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| 研究分担者 |
Pyshkin Evgeny 会津大学, コンピュータ理工学部, 上級准教授 (50794088)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2022年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | NLG / trend descriptions / describing graphs / intelligent CALL / data series description / NLP / language generation |
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| 研究開始時の研究の概要 |
This interactive online tool provides unlimited practice opportunities to describe graphs and charts. Students can practice at three levels: word, clause or sentence using generated practice texts. Students either fill in the gaps, complete sentence stems or draft the whole text. On completion of their practice task, they compare their answers with an automatically generated plain or colorized exemplar text. This helps learners notice patterns, which is said to be a precursor to learning.
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| 研究実績の概要 |
We made use of the corpus of trend descriptions to extract prototypical rhetorical patterns of trend series descriptions and the relative frequency of functional exponents that are used to realize these.
We extended the codebase of the description generator, which is now able to generate trend descriptions at five proficiency levels from beginner through to upper intermediate using rule-based parsing of a spreadsheet. As the proficiency level increases so does sentence complexity, grammatical variety and vocabulary range. Users can switch between levels to see how the trend description changes with language proficiency. Our next step is to explore the use of a large language model to generate suitable descriptions for advanced language learners.
We have also developed a work flow to streamline AI-generated video explanations to accompany the generated texts.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
Progress on the software has exceeded our initial expectations, and even have also developed a smoothing algorithm that reduces the number of data points to enables a description to be created even if there are hundreds or thousands of data points.
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| 今後の研究の推進方策 |
We expect to place the prototype online in the next few months, and conduct experiments on its accuracy, usability and efficacy.
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