Zeitschriftenartikel zum Thema „Pre-training corpora“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Pre-training corpora" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Sun, Yu, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu und Haifeng Wang. „ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 05 (03.04.2020): 8968–75. http://dx.doi.org/10.1609/aaai.v34i05.6428.
Der volle Inhalt der QuelleMoodaley, Wayne, und Arnesh Telukdarie. „A Conceptual Framework for Subdomain Specific Pre-Training of Large Language Models for Green Claim Detection“. European Journal of Sustainable Development 12, Nr. 4 (01.10.2023): 319. http://dx.doi.org/10.14207/ejsd.2023.v12n4p319.
Der volle Inhalt der QuelleLiu, Yinhan, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis und Luke Zettlemoyer. „Multilingual Denoising Pre-training for Neural Machine Translation“. Transactions of the Association for Computational Linguistics 8 (November 2020): 726–42. http://dx.doi.org/10.1162/tacl_a_00343.
Der volle Inhalt der QuelleDean, Roger Thornton, und Marcus Thomas Pearce. „Algorithmically-generated Corpora that use Serial Compositional Principles Can Contribute to the Modeling of Sequential Pitch Structure in Non-tonal Music“. Empirical Musicology Review 11, Nr. 1 (08.07.2016): 27. http://dx.doi.org/10.18061/emr.v11i1.4900.
Der volle Inhalt der QuelleYuan, Sha, Hanyu Zhao, Zhengxiao Du, Ming Ding, Xiao Liu, Yukuo Cen, Xu Zou, Zhilin Yang und Jie Tang. „WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models“. AI Open 2 (2021): 65–68. http://dx.doi.org/10.1016/j.aiopen.2021.06.001.
Der volle Inhalt der QuelleKreutzer, Julia, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo et al. „Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets“. Transactions of the Association for Computational Linguistics 10 (2022): 50–72. http://dx.doi.org/10.1162/tacl_a_00447.
Der volle Inhalt der QuelleQian, Jing, Yong Yue, Katie Atkinson und Gangmin Li. „Understanding Chinese Moral Stories with Further Pre-Training“. International Journal on Natural Language Computing 12, Nr. 2 (29.04.2023): 01–12. http://dx.doi.org/10.5121/ijnlc.2023.12201.
Der volle Inhalt der QuelleJiang, Xiaoze, Yaobo Liang, Weizhu Chen und Nan Duan. „XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 10 (28.06.2022): 10840–48. http://dx.doi.org/10.1609/aaai.v36i10.21330.
Der volle Inhalt der QuelleKajiwara, Tomoyuki, Biwa Miura und Yuki Arase. „Monolingual Transfer Learning via Bilingual Translators for Style-Sensitive Paraphrase Generation“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 05 (03.04.2020): 8042–49. http://dx.doi.org/10.1609/aaai.v34i05.6314.
Der volle Inhalt der QuelleKryeziu, Labehat, und Visar Shehu. „Pre-Training MLM Using Bert for the Albanian Language“. SEEU Review 18, Nr. 1 (01.06.2023): 52–62. http://dx.doi.org/10.2478/seeur-2023-0035.
Der volle Inhalt der QuelleShi, Peng, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos Santos und Bing Xiang. „Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 15 (18.05.2021): 13806–14. http://dx.doi.org/10.1609/aaai.v35i15.17627.
Der volle Inhalt der QuelleAlruwaili, Awatif. „An online training course on the use of corpora for teachers in public schools“. JALT CALL Journal 19, Nr. 1 (April 2023): 53–70. http://dx.doi.org/10.29140/jaltcall.v19n1.675.
Der volle Inhalt der QuelleLuo, Da, Yanglei Gan, Rui Hou, Run Lin, Qiao Liu, Yuxiang Cai und Wannian Gao. „Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 17 (24.03.2024): 18742–50. http://dx.doi.org/10.1609/aaai.v38i17.29838.
Der volle Inhalt der QuelleLi, Zhen, Dan Qu, Chaojie Xie, Wenlin Zhang und Yanxia Li. „Language Model Pre-training Method in Machine Translation Based on Named Entity Recognition“. International Journal on Artificial Intelligence Tools 29, Nr. 07n08 (30.11.2020): 2040021. http://dx.doi.org/10.1142/s0218213020400217.
Der volle Inhalt der QuelleLiu, Peng, Lemei Zhang und Jon Atle Gulla. „Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems“. Transactions of the Association for Computational Linguistics 11 (2023): 1553–71. http://dx.doi.org/10.1162/tacl_a_00619.
Der volle Inhalt der QuelleMaruyama, Takumi, und Kazuhide Yamamoto. „Extremely Low-Resource Text Simplification with Pre-trained Transformer Language Model“. International Journal of Asian Language Processing 30, Nr. 01 (März 2020): 2050001. http://dx.doi.org/10.1142/s2717554520500010.
Der volle Inhalt der QuelleZheng, Yinhe, Rongsheng Zhang, Minlie Huang und Xiaoxi Mao. „A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 05 (03.04.2020): 9693–700. http://dx.doi.org/10.1609/aaai.v34i05.6518.
Der volle Inhalt der QuelleMao, Zhuoyuan, Chenhui Chu und Sadao Kurohashi. „Linguistically Driven Multi-Task Pre-Training for Low-Resource Neural Machine Translation“. ACM Transactions on Asian and Low-Resource Language Information Processing 21, Nr. 4 (31.07.2022): 1–29. http://dx.doi.org/10.1145/3491065.
Der volle Inhalt der QuelleAi, Xi, und Bin Fang. „Empirical Regularization for Synthetic Sentence Pairs in Unsupervised Neural Machine Translation“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 14 (18.05.2021): 12471–79. http://dx.doi.org/10.1609/aaai.v35i14.17479.
Der volle Inhalt der QuelleFromont, Robert, und Kevin Watson. „Factors influencing automatic segmental alignment of sociophonetic corpora“. Corpora 11, Nr. 3 (November 2016): 401–31. http://dx.doi.org/10.3366/cor.2016.0101.
Der volle Inhalt der QuelleZhu, Quan, Xiaoyin Wang, Xuan Liu, Wanru Du und Xingxing Ding. „Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus“. Mathematical Biosciences and Engineering 20, Nr. 10 (2023): 18566–91. http://dx.doi.org/10.3934/mbe.2023824.
Der volle Inhalt der QuelleSiddhant, Aditya, Anuj Goyal und Angeliki Metallinou. „Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 4959–66. http://dx.doi.org/10.1609/aaai.v33i01.33014959.
Der volle Inhalt der QuelleGao, Yunfan, Yun Xiong, Siqi Wang und Haofen Wang. „GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest“. Applied Sciences 12, Nr. 24 (16.12.2022): 12942. http://dx.doi.org/10.3390/app122412942.
Der volle Inhalt der QuelleChiang, Cheng-Han, und Hung-yi Lee. „On the Transferability of Pre-trained Language Models: A Study from Artificial Datasets“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 10 (28.06.2022): 10518–25. http://dx.doi.org/10.1609/aaai.v36i10.21295.
Der volle Inhalt der QuelleLi, Yucheng, Frank Guerin und Chenghua Lin. „LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 17 (24.03.2024): 18600–18607. http://dx.doi.org/10.1609/aaai.v38i17.29822.
Der volle Inhalt der QuelleKarimzadeh, Morteza, und Alan MacEachren. „GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora“. ISPRS International Journal of Geo-Information 8, Nr. 4 (27.03.2019): 161. http://dx.doi.org/10.3390/ijgi8040161.
Der volle Inhalt der QuelleBae, Jae Kwon. „A Study on Application of the Artificial Intelligence-Based Pre-trained Language Model“. Academic Society of Global Business Administration 21, Nr. 2 (30.04.2024): 64–83. http://dx.doi.org/10.38115/asgba.2024.21.2.64.
Der volle Inhalt der QuelleFang, Liuqin, Qing Ma und Jiahao Yan. „The effectiveness of corpus-based training on collocation use in L2 writing for Chinese senior secondary school students“. Journal of China Computer-Assisted Language Learning 1, Nr. 1 (01.08.2021): 80–109. http://dx.doi.org/10.1515/jccall-2021-2004.
Der volle Inhalt der QuelleKang, Yu, Tianqiao Liu, Hang Li, Yang Hao und Wenbiao Ding. „Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 10 (28.06.2022): 10875–83. http://dx.doi.org/10.1609/aaai.v36i10.21334.
Der volle Inhalt der QuelleHe, Wanwei, Yinpei Dai, Yinhe Zheng, Yuchuan Wu, Zheng Cao, Dermot Liu, Peng Jiang et al. „GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 10 (28.06.2022): 10749–57. http://dx.doi.org/10.1609/aaai.v36i10.21320.
Der volle Inhalt der QuelleGarrido-Muñoz , Ismael, Arturo Montejo-Ráez , Fernando Martínez-Santiago und L. Alfonso Ureña-López . „A Survey on Bias in Deep NLP“. Applied Sciences 11, Nr. 7 (02.04.2021): 3184. http://dx.doi.org/10.3390/app11073184.
Der volle Inhalt der QuellePerkowski, Ernest, Rui Pan, Tuan Dung Nguyen, Yuan-Sen Ting, Sandor Kruk, Tong Zhang, Charlie O’Neill et al. „AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets“. Research Notes of the AAS 8, Nr. 1 (08.01.2024): 7. http://dx.doi.org/10.3847/2515-5172/ad1abe.
Der volle Inhalt der QuelleWang, Ke, Xiutian Zhao und Wei Peng. „Learning from Failure: Improving Meeting Summarization without Good Samples“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 17 (24.03.2024): 19153–61. http://dx.doi.org/10.1609/aaai.v38i17.29883.
Der volle Inhalt der QuellePota, Marco, Mirko Ventura, Rosario Catelli und Massimo Esposito. „An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian“. Sensors 21, Nr. 1 (28.12.2020): 133. http://dx.doi.org/10.3390/s21010133.
Der volle Inhalt der QuelleGonzález-Docasal, Ander, und Aitor Álvarez. „Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics“. Applied Sciences 13, Nr. 14 (10.07.2023): 8049. http://dx.doi.org/10.3390/app13148049.
Der volle Inhalt der QuelleVu, Dang Thanh, Gwanghyun Yu, Chilwoo Lee und Jinyoung Kim. „Text Data Augmentation for the Korean Language“. Applied Sciences 12, Nr. 7 (28.03.2022): 3425. http://dx.doi.org/10.3390/app12073425.
Der volle Inhalt der QuelleQi, Kunxun, und Jianfeng Du. „Translation-Based Matching Adversarial Network for Cross-Lingual Natural Language Inference“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 05 (03.04.2020): 8632–39. http://dx.doi.org/10.1609/aaai.v34i05.6387.
Der volle Inhalt der QuelleA. Brenes, Jose, Javier Ferrández-Pastor, José M. Cámara-Zapata und Gabriela Marín-Raventós. „Use of Hough Transform and Homography for the Creation of Image Corpora for Smart Agriculture“. International Journal on Cybernetics & Informatics 12, Nr. 6 (07.10.2023): 09–19. http://dx.doi.org/10.5121/ijci.2023.120602.
Der volle Inhalt der QuelleYang, Tiancheng, Ilia Sucholutsky, Kuang-Yu Jen und Matthias Schonlau. „exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies“. PeerJ Computer Science 10 (28.02.2024): e1888. http://dx.doi.org/10.7717/peerj-cs.1888.
Der volle Inhalt der QuelleLi, Lei, Yongfeng Zhang und Li Chen. „Personalized Prompt Learning for Explainable Recommendation“. ACM Transactions on Information Systems 41, Nr. 4 (23.03.2023): 1–26. http://dx.doi.org/10.1145/3580488.
Der volle Inhalt der QuellePanboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern und Peerapon Vateekul. „Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning“. Remote Sensing 11, Nr. 1 (04.01.2019): 83. http://dx.doi.org/10.3390/rs11010083.
Der volle Inhalt der QuellePanboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern und Peerapon Vateekul. „Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images“. Remote Sensing 13, Nr. 24 (15.12.2021): 5100. http://dx.doi.org/10.3390/rs13245100.
Der volle Inhalt der QuelleLiu, Rui, und Barzan Mozafari. „Transformer with Memory Replay“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 7 (28.06.2022): 7567–75. http://dx.doi.org/10.1609/aaai.v36i7.20722.
Der volle Inhalt der QuelleLiu, Weijie, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng und Ping Wang. „K-BERT: Enabling Language Representation with Knowledge Graph“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 03 (03.04.2020): 2901–8. http://dx.doi.org/10.1609/aaai.v34i03.5681.
Der volle Inhalt der QuellePeng, Baolin, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden und Jianfeng Gao. „Soloist: BuildingTask Bots at Scale with Transfer Learning and Machine Teaching“. Transactions of the Association for Computational Linguistics 9 (2021): 807–24. http://dx.doi.org/10.1162/tacl_a_00399.
Der volle Inhalt der QuellePalagin, O. V., V. Yu Velychko, K. S. Malakhov und O. S. Shchurov. „Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach“. PROBLEMS IN PROGRAMMING, Nr. 2-3 (September 2020): 341–51. http://dx.doi.org/10.15407/pp2020.02-03.341.
Der volle Inhalt der QuelleChoi, Yong-Seok, Yo-Han Park, Seung Yun, Sang-Hun Kim und Kong-Joo Lee. „Factors Behind the Effectiveness of an Unsupervised Neural Machine Translation System between Korean and Japanese“. Applied Sciences 11, Nr. 16 (21.08.2021): 7662. http://dx.doi.org/10.3390/app11167662.
Der volle Inhalt der QuelleZayed, Abdelrahman, Prasanna Parthasarathi, Gonçalo Mordido, Hamid Palangi, Samira Shabanian und Sarath Chandar. „Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 12 (26.06.2023): 14593–601. http://dx.doi.org/10.1609/aaai.v37i12.26706.
Der volle Inhalt der QuelleKeung, Phillip, Julian Salazar, Yichao Lu und Noah A. Smith. „Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings“. Transactions of the Association for Computational Linguistics 8 (Dezember 2020): 828–41. http://dx.doi.org/10.1162/tacl_a_00348.
Der volle Inhalt der QuelleLaucis, Rolands, und Gints Jēkabsons. „Evaluation of Word Embedding Models in Latvian NLP Tasks Based on Publicly Available Corpora“. Applied Computer Systems 26, Nr. 2 (01.12.2021): 132–38. http://dx.doi.org/10.2478/acss-2021-0016.
Der volle Inhalt der Quelle