Argumentative Writing Support System for EFL Learners
| Project/Area Number |
20J13239
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 02100:Foreign language education-related
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
PUTRA JAN WIRA GOTAMA 東京工業大学, 情報理工学院, 特別研究員(DC2)
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| Project Period (FY) |
2020-04-24 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2021: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Argumentative Structure / EFL learners / ICNALE-AS2R Corpus / Annotation / Sentence Reordering / Structure Parsing / EFL students / argumentative structure / annotation tool / TIARA / annotator agreement |
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| Outline of Research at the Start |
This research aims to develop a tool to enable non-native English speakers in writing better argumentative essays (in English). Specifically, it works by arranging sentences in the essay such that it results in a better logically-structured text.
Our main approach is to analyse the argumentative structure. Argumentative structure analysis aims to understand how sentences are connected to each other when forming argument as a whole. The way how to order sentences properly can be inferred from such structure. The tool is useful especially for helping teacher in giving feedback to students.
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| Outline of Annual Research Achievements |
My research aims to develop a support tool for analysis and improving argumentative essays written by English-as-foreign-language (EFL) learners. The tool extracts and visualizes the argumentative structure of a given essay, and then suggests an improved version of the essay by reordering sentences. This project was divided into several steps:
(a) Dataset Construction. In 2020, I constructed the first corpus of 434 essays written by EFL learners (ICNALE-AS2R), which have been annotated with argumentative structure and sentence reordering. I also developed an in-house annotation software (open-source) TIARA for this step.
(b) Structure Parsing Model. Given an input essay, I developed a deep learning system to predict the argumentative structure of the essay. I used the combination of Biaffine Attention and BERT models. I also proposed multi-task and multi-corpora training strategies which enhanced the parsing performance of the base model. The idea was to guide the model on how to reduce the search space and to utilise existing non-EFL datasets via selective sampling.
(c) Sentence Reordering Model. Given an essay and its corresponding argumentative structure as input, I proposed a reordering system based on the linguistic theory of coherence. The task was formulated as a traversal problem, containing two steps. The first is a pairwise ordering constraint task between pairs of sentences (tackled using ALBERT model). The second is a traversal (output generation) step using an ad-hoc algorithm. Detailed empirical evaluation showed that my proposed system has a good potential.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(13 results)
