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A multilayer convolutional encoder-decoder neural network for grammatical error correction. arXiv:1801.08831 (2018).  S. Chollampatt and H.T. Ng. 2018. A multilayer convolutional encoder-decoder neural network for grammatical error correction. arXiv:1801.08831 (2018).","DOI":"10.18653\/v1\/D18-1274"},{"key":"e_1_3_2_2_6_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805","author":"Devlin J.","year":"2019","unstructured":"J. Devlin , M.W. Chang , K. Lee , and K. Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2019). J. Devlin, M.W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2019)."},{"key":"e_1_3_2_2_7_1","unstructured":"Bijaya et. al. 2018a. Sub2Vec: Feature Learning for Subgraphs. In KDD.  Bijaya et. al. 2018a. Sub2Vec: Feature Learning for Subgraphs. 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In Text Summarization Branches Out."},{"key":"e_1_3_2_2_28_1","volume-title":"Paths Omitted","author":"Liu Yang","year":"2020","unstructured":"Yang Liu , Tim Althoff , and Jeffrey Heer . 2020 a. Paths Explored , Paths Omitted , Paths Obscured : Decision Points & Selective Reporting in End-to-End Data Analysis. CHI ( 2020 ). Yang Liu, Tim Althoff, and Jeffrey Heer. 2020 a. Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis. CHI (2020)."},{"key":"e_1_3_2_2_29_1","unstructured":"Yang Liu Alex Kale Tim Althoff and Jeffrey Heer. 2020 b. Boba: Authoring and visualizing multiverse analyses. arxiv: cs.HC\/2007.05551  Yang Liu Alex Kale Tim Althoff and Jeffrey Heer. 2020 b. 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