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[Paper] “Improving logical flow in English-as-a-foreign-language learner essays by reordering sentences” published online by WRH professors Takenobu Tokunaga and Simone Teufel

WRH professors Takenobu Tokunaga and Simone Teufel (School of Computing) published a paper in Artificial Intelligence.

“Improving logical flow in English-as-a-foreign-language learner essays by reordering sentences”

Argumentation is ubiquitous in everyday discourse, and it is a skill that can be learned. In our society, it is also one that must be learned: education systems all over the world agree on the importance of argumentation skills. However, writing effective argumentation is difficult, and even more so if it has to be expressed in a foreign language. Existing artificial intelligence systems for language learning can help learners: they can provide objective feedback (e.g., concerning grammar and spelling), as well as providing learners with opportunities to identify errors and subsequently improve their texts. Even so, systems aiming at higher discourse-level skills, such as persuasiveness and content organisation, are still limited. In this article, we propose the novel task of sentence reordering for improving the logical flow of argumentative essays. To train such a computational system, we present a new corpus called ICNALE-AS2R, containing essays written by English-as-foreign-language learners from various Asian countries, that have been annotated with argumentative structure and sentence reordering. We also propose a novel method to automatically reorder sentences in imperfect essays, which is based on argumentative structure analysis. Given an input essay and its corresponding argumentative structure, we cast the reordering task as a traversal problem. Our sentence reordering system first determines the pairwise ordering relation between pairs of sentences that are connected by argumentative relations. In the second step, the system traverses the argumentative structure that has been augmented with pairwise ordering information, in order to generate the final output text. Empirical evaluation shows that in the task of reconstructing the final reordered essays in the dataset, our reordering system achieves .926 and .879 in longest common subsequence ratio and Kendall’s Tau metrics, respectively. The system is also able to perform the reordering operation selectively, that is, it reorders sentences when necessary and retains the original input order when it is already optimal.



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