Papers by Yuning Ding

6 papers
When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing (2024.lrec-main)

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Challenge: Argumentative essays require a high degree of cohesion, defined as a network of semantic relationships that link together.
Approach: They investigate the role of arguments in the automatic scoring of cohesion in argumentative essays.
Outcome: The proposed model improves on a multi-task learning process by adding argumentative elements as an auxiliary task.
DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays (2024.lrec-main)

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Challenge: Existing corpora focus on specific out-of-school domains, such as legal documents.
Approach: They present a digital argumentation instruction for science corpus on 4589 essays written by 1839 german secondary school students.
Outcome: The proposed corpus is annotated according to a fine-grained annotation scheme on 4589 essays written by 1839 german secondary school students.
Don’t take “nswvtnvakgxpm” for an answer –The surprising vulnerability of automatic content scoring systems to adversarial input (2020.coling-main)

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Challenge: Automated content scoring systems can be used on short answer tasks to save human effort, but can invite cheating strategies such as writing irrelevant answers.
Approach: They generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and examine countermeasures such as adversarials.
Outcome: The proposed methods show that even simple methods can reduce content scoring performance but do not solve the problem.
FEAT-writing: An Interactive Training System for Argumentative Writing (2025.coling-demos)

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Challenge: Argumentative writing is a critical skill for academic success, but many students struggle to develop these skills.
Approach: They developed an online system that provides students with automated feedback and exercises for argumentative writing.
Outcome: The proposed system improves argumentative writing quality among native English speakers and english-as-a-foreign-language learners.
Score It All Together: A Multi-Task Learning Study on Automatic Scoring of Argumentative Essays (2023.findings-acl)

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Challenge: a multi-task learning approach outperforms sequential approaches for scoring argumentative essays . segmentation and classification of argumentative elements are important steps towards providing feedback on writing structure, but assessing the quality of arguments is less researched .
Approach: They use a student essay dataset to study how argumentative essays are scored . they use automated span detection, type and quality prediction to combine these tasks .
Outcome: The proposed method outperforms sequential approaches for segmentation and quality prediction.
Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels (2020.aacl-main)

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Challenge: Unlike English, which uses spaces as natural separators between words, segmentation of Chinese texts into tokens is challenging.
Approach: They present two data sets for Chinese content scoring that use Chinese short answer questions and a new scoring system that uses Chinese short-answer questions.
Outcome: The proposed system performs better on lower segmentation levels than on token level.

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