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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 – 6 of 6 1 Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ... Alabi, Jesujoba O.; Adelani, David Ifeoluwa; Mosbach, Marius. - : arXiv, 2022 BASE Show details 2 Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study ... Abdullah, Badr M.; Mosbach, Marius; Zaitova, Iuliia. - : arXiv, 2021 BASE Show details 3 On the Correlation of Context-Aware Language Models With the Intelligibility of Polish Target Words to Czech Readers Jágrová, Klára; Hedderich, Michael; Mosbach, Marius... In: Front Psychol (2021) BASE Show details 4 On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers ... Mosbach, Marius; Khokhlova, Anna; Hedderich, Michael A.. - : arXiv, 2020 BASE Show details 5 A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ... Mosbach, Marius; Degaetano-Ortlieb, Stefania; Krielke, Marie-Pauline; Abdullah, Badr M.; Klakow, Dietrich. - : arXiv, 2020 Abstract: Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance. Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models' performance. Our results highlight the importance of (a)model comparison in evaluation task and (b) building up claims of model performance and the ... : Accepted to COLING 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2011.00960 https://arxiv.org/abs/2011.00960 BASE Hide details 6 A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ... The 28th International Conference on Computational Linguistics 2020; Abdullah, Badr; Degaetano-Ortlieb, Stefania. - : Underline Science Inc., 2020 BASE Show details

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