# Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers

arXiv: https://arxiv.org/abs/2607.16085 · HTML: https://mlsanity.com/paper/2607.16085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [On the Foundations of Shortcut Learning](https://mlsanity.com/paper/2310.16228/) (2023)
- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input](https://mlsanity.com/paper/1804.06898/) (2018)
- [Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback](https://mlsanity.com/paper/2412.11986/) (2024)
- [Assessment of L2 Oral Proficiency using Speech Large Language Models](https://mlsanity.com/paper/2505.21148/) (2025)
- [Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals](https://mlsanity.com/paper/2405.19433/) (2024)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)
- [Natural Language-based Assessment of L2 Oral Proficiency using LLMs](https://mlsanity.com/paper/2507.10200/) (2025)
- [AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses](https://mlsanity.com/paper/2109.11728/) (2021)
- [Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessments](https://mlsanity.com/paper/2505.23315/) (2025)
- [Who Needs Decoders? Efficient Estimation of Sequence-level Attributes](https://mlsanity.com/paper/2305.05098/) (2023)
- [FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance](https://mlsanity.com/paper/2602.02060/) (2026)

## Abstract (arXiv)

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 their score by exploiting the shortcut rather than improving their ability. This paper introduces a novel training criterion that is able to reduce the classifier's reliance on shortcuts, thus for example limiting this option for malpractice in language assessment. This process is illustrated on two forms of assessment system, one based on the audio the other on the speech recognition text. The results show that, for both systems, there is higher correlations with features that could be exploited for malpractice than expected from the human reference, indicating an over-reliance on these features. By introducing the modified training criterion, this correlation can be reduced to be closer to the reference correlation.