Papers with anomaly detection task

3 papers
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem (2020.lrec-1)

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Challenge: Dialogue systems for interaction with humans are becoming more popular . the best way to estimate their success is through means of human evaluation .
Approach: They investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task.
Outcome: The proposed approach is based on four models and shows negative results . the proposed approach could be used in the future to improve human-led dialogue evaluations.
AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing (2023.emnlp-main)

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Challenge: Methods for Anomaly Detection in text have shown strong empirical results on ad-hoc anomaly setups that are usually made by downsampling some classes of a labeled dataset.
Approach: They propose a unified benchmark for detecting various types of anomalies . they evaluate two strong shallow baselines and two current state-of-the-art neural approaches .
Outcome: The proposed benchmarks provide insights into the knowledge the neural models are learning when performing the task.
SA-CLIP: Language Guided Image Spatial and Action Feature Learning (2025.findings-emnlp)

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Challenge: Contrastive language-image pretraining models struggle with real-world downstream tasks such as road traffic anomaly detection due to inability to effectively capture spatial and action relationships between objects within images.
Approach: They compile and curate a dataset and train a Spatial and Action relationship aware CLIP model.
Outcome: The proposed model performs well on the traffic anomaly detection task .

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