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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Artificial Text Detection via Examining the Topology of Attention Maps Kushnareva, Laida; Cherniavskii, Daniil; Mikhailov, Vladislav... In: ACL Anthology ; Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03456191 ; Empirical Methods in Natural Language Processing, ACL (Association for Computational Linguistics), Nov 2021, Punta Cana, Dominican Republic (2021) BASE Show details 2 Shaking Syntactic Trees on the Sesame Street: Multilingual Probing with Controllable Perturbations ... Taktasheva, Ekaterina; Mikhailov, Vladislav; Artemova, Ekaterina. - : arXiv, 2021 BASE Show details 3 RuSentEval: Linguistic Source, Encoder Force! ... Mikhailov, Vladislav; Taktasheva, Ekaterina; Sigdel, Elina. - : arXiv, 2021 BASE Show details 4 Morph Call: Probing Morphosyntactic Content of Multilingual Transformers ... Mikhailov, Vladislav; Serikov, Oleg; Artemova, Ekaterina. - : arXiv, 2021 BASE Show details 5 Artificial Text Detection via Examining the Topology of Attention Maps ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cherniavskii, Daniil; Mikhailov, Vladislav. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.50/ Abstract: The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detection, they still lack interpretability and robustness towards unseen models. To this end, we propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) which is currently understudied in the field of NLP. We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10\% on three common datasets, and tend to be the most robust towards unseen GPT-style generation models as opposed to existing methods. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties. The results demonstrate ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37803-artificial-text-detection-via-examining-the-topology-of-attention-maps https://dx.doi.org/10.48448/yayj-8t90 BASE Hide details

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