| 研究実績の概要 |
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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