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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 – 11 of 11 1 Learning How to Translate North Korean through South Korean ... Kim, Hwichan; Moon, Sangwhan; Okazaki, Naoaki. - : arXiv, 2022 BASE Show details 2 Joint Optimization of Tokenization and Downstream Model ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hiraoka, Tatsuya; Keyaki, Atsushi. - : Underline Science Inc., 2021 BASE Show details 3 Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts ... Bugliarello, Emanuele; Cotterell, Ryan; Okazaki, Naoaki. - : ETH Zurich, 2021 BASE Show details 4 Transformer-based Lexically Constrained Headline Generation ... Yamada, Kosuke; Hitomi, Yuta; Tamori, Hideaki. - : arXiv, 2021 BASE Show details 5 Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts Okazaki, Naoaki; Bugliarello, Emanuele; Cotterell, Ryan; Elliott, Desmond In: Transactions of the Association for Computational Linguistics, 9 (2021) Abstract: Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of methods have been proposed for pretraining vision and language BERTs to tackle challenges at the intersection of these two key areas of AI. These models can be categorized into either single-stream or dual-stream encoders. We study the differences between these two categories, and show how they can be unified under a single theoretical framework. We then conduct controlled experiments to discern the empirical differences between five vision and language BERTs. Our experiments show that training data and hyperparameters are responsible for most of the differences between the reported results, but they also reveal that the embedding layer plays a crucial role in these massive models. ; ISSN:2307-387X URL: https://hdl.handle.net/20.500.11850/517633 https://doi.org/10.3929/ethz-b-000517633 BASE Hide details 6 Transformer-based Lexically Constrained Headline Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hitomi, Yuta; Inui, Kentaro. - : Underline Science Inc., 2021 BASE Show details 7 It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ... Bugliarello, Emanuele; Mielke, Sabrina J.; Anastasopoulos, Antonios. - : ETH Zurich, 2020 BASE Show details 8 It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information Mielke, Sabrina J.; Okazaki, Naoaki; Anastasopoulos, Antonios... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 9 The mechanism of additive composition [<Journal>] Tian, Ran [Verfasser]; Okazaki, Naoaki [Sonstige]; Inui, Kentaro [Sonstige] DNB Subject Category Language Show details 10 Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization ... Sasaki, Akira; Hanawa, Kazuaki; Okazaki, Naoaki. - : arXiv, 2017 BASE Show details 11 A preference learning approach to sentence ordering for multi-document summarization Bollegala, Danushka; Okazaki, Naoaki; Ishizuka, Mitsuru In: Information sciences. - New York, NY : Elsevier Science Inc. 217 (2012), 78-95 OLC Linguistik Show details

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