Dataset Groups Activity Stream Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention The proposed model consists of three subsystems: Feature Extractor, Attention-based Classification Model, and Lexical Stress Error Detector. BibTex: @dataset{Daniel_Korzekwa_and_Roberto_Barra-Chicote_and_Szymon_Zaporowski_and_Grzegorz_Beringer_and_Jaime_Lorenzo-Trueba_and_Alicja_Serafinowicz_and_Jasha_Droppo_and_Thomas_Drugman_and_Bozena_Kostek_2024, abstract = {The proposed model consists of three subsystems: Feature Extractor, Attention-based Classification Model, and Lexical Stress Error Detector.}, author = {Daniel Korzekwa and Roberto Barra-Chicote and Szymon Zaporowski and Grzegorz Beringer and Jaime Lorenzo-Trueba and Alicja Serafinowicz and Jasha Droppo and Thomas Drugman and Bozena Kostek}, doi = {10.57702/7t9bdzpa}, institution = {No Organization}, keyword = {'Attention', 'Data Augmentation', 'Lexical Stress Error Detection', 'Non-Native English'}, month = {dec}, publisher = {TIB}, title = {Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention}, url = {https://service.tib.eu/ldmservice/dataset/detection-of-lexical-stress-errors-in-non-native--l2--english-with-data-augmentation-and-attention}, year = {2024} }