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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 – 8 of 8 1 Learning How to Translate North Korean through South Korean ... Kim, Hwichan; Moon, Sangwhan; Okazaki, Naoaki. - : arXiv, 2022 BASE Show details 2 From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ... van der Goot, Rob; Sharaf, Ibrahim; Imankulova, Aizhan. - : arXiv, 2021 BASE Show details 3 Neural Combinatory Constituency Parsing ... 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; Chen, Zhousi; Imankulova, Aizhan; Komachi, Mamoru; Zhang, Longtu. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.194 Abstract: We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theoretical sub-quadratic complexity and empirical linear complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. Both the models with XLNet provide near state-of-the-art accuracies for English. Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese). ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/26285-neural-combinatory-constituency-parsing https://dx.doi.org/10.48448/r4zf-ny24 BASE Hide details 4 Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model ... Katsumata, Satoru; Komachi, Mamoru. - : arXiv, 2020 BASE Show details 5 Cross-lingual Transfer Learning for Grammatical Error Correction ... The 28th International Conference on Computational Linguistics 2020; Imankulova, Aizhan; Kaneko, Masahiro. - : Underline Science Inc., 2020 BASE Show details 6 Multi-task Learning for Japanese Predicate Argument Structure Analysis ... Omori, Hikaru; Komachi, Mamoru. - : arXiv, 2019 BASE Show details 7 Japanese Predicate Conjugation for Neural Machine Translation ... Kurosawa, Michiki; Matsumura, Yukio; Yamagishi, Hayahide. - : arXiv, 2018 BASE Show details 8 UniDic for Early Middle Japanese: a Dictionary for Morphological Analysis of Classical Japanese Ogiso, Toshinobu; Komachi, Mamoru; Den, Yasuharu... In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, 23 - 25 May 2012 (2012), 911-915 IDS OBELEX meta Show details

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