| Project/Area Number |
22K00792
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 02100:Foreign language education-related
|
|---|
| Research Institution | The University of Aizu |
|---|
Principal Investigator |
BLAKE John 会津大学, コンピュータ理工学部, 上級准教授 (80635954)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
Pyshkin Evgeny 会津大学, コンピュータ理工学部, 上級准教授 (50794088)
|
|---|
| Project Period (FY) |
2022-04-01 – 2025-03-31
|
| Project Status |
Granted (Fiscal Year 2023)
|
| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
|---|
| Keywords | NLG / trend descriptions / describing graphs / intelligent CALL / data series description / NLP / language generation |
|---|
| Outline of Research at the Start |
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.
|
| Outline of Annual Research Achievements |
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.
|
| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
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.
|
| Strategy for Future Research Activity |
We expect to place the prototype online in the next few months, and conduct experiments on its accuracy, usability and efficacy.
|
