Papers by Brian Riordan

2 papers
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.
Atypical Inputs in Educational Applications (N18-3)

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Challenge: atypical characteristics of some responses make it difficult for an automated scoring system to assign a valid score . a typical spoken response with a lot of background noise may suffer from frequent errors in automated speech recognition .
Approach: They propose a pipeline that detects and processes non-scorable responses at run-time . they also propose linguistic filtering models for spoken responses in language tests .
Outcome: The proposed pipeline detects and processes non-scorable responses at run-time and evaluates them for spoken responses in language proficiency assessment.

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