Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers
Increasingly, speech and language processing tasks take either audio or text directly rather than extracting features from these as the input to the classifier or regressor. Often these systems make use of complex, for example transformer-based, processes that have the ability to derive highly non-linear mappings between the input and the output. Unfortunately these systems can also learn ''shortcuts'' where the classifier is overly reliant on particular aspects of the input to yield the output. For the task of language proficiency assessment, this over-reliance can enable learners to increase
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- PossiblePossibly related (embedding) · 50%What exactly does word2vec learn? →
- LinkedLinked via arxiv author · 85%Shilin Gao →
“Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers”
- LinkedLinked via arxiv author · 85%Mark J. F. Gales →
“Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers”
- LinkedLinked via arxiv author · 85%Kate M. Knill →
“Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers”
- FuzzySimilar title/name (fuzzy) · 87%rockbenben/ChatGPT-Shortcut →
“Fuzzy title match (0.94): “Controlling Implicit Shortcut Reliance in L2 Spoken English ” ≈ “rockbenben/ChatGPT-Shortcut””