Papers by Andrei Manolache
AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing (2023.emnlp-main)
Copied to clipboard
| 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. |
Rethinking the Authorship Verification Experimental Setups (2022.emnlp-main)
Copied to clipboard
| Challenge: | Identifying the author of a text is one of the most versatile NLP tasks, with applications ranging from plagiarism detection to forensics and monitoring the activity of cyber-criminals. |
| Approach: | They propose five new public splits over the PAN dataset to isolate and identify biases related to the text topic and to the author’s writing style. |
| Outcome: | The proposed models are competitive with state-of-the-art methods and generalize better on dark reddit datasets. |
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent deep learning methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. |
| Approach: | They propose to use a novel pretext task to learn a deep learning model for Anomaly Detection in text to train a model to discriminate between different transformations applied to visual data. |
| Outcome: | The proposed method outperforms state-of-the-art methods on 20Newsgroups and AG News datasets in the semi-supervised setting and in the unsupervised setting. |