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Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
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ExpMRC: explainability evaluation for machine reading comprehension
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In: Heliyon (2022)
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Multilingual multi-aspect explainability analyses on machine reading comprehension models
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In: iScience (2022)
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Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
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Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ...
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Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
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Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
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Neural Stylistic Response Generation with Disentangled Latent Variables ...
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Language learners' enjoyment and emotion regulation in online collaborative learning
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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ...
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TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
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N-LTP: An Open-source Neural Language Technology Platform for Chinese ...
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CoNLL 2018 Shared Task System Outputs
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Zeman, Daniel; Potthast, Martin; Duthoo, Elie; Mesnard, Olivier; Rybak, Piotr; Wróblewska, Alina; Che, Wanxiang; Liu, Yijia; Wang, Yuxuan; Zheng, Bo; Liu, Ting; Li, Zuchao; He, Shexia; Zhang, Zhuosheng; Zhao, Hai; Wu, Yingting; Tong, Jia-Jun; Nguyen, Dat Quoc; Verspoor, Karin; Wan, Hui; Naseem, Tahira; Lee, Young-Suk; Castelli, Vittorio; Ballesteros, Miguel; Hershcovich, Daniel; Abend, Omri; Rappoport, Ari; Smith, Aaron; Bohnet, Bernd; de Lhoneux, Miryam; Nivre, Joakim; Shao, Yan; Stymne, Sara; Kırnap, Ömer; Dayanık, Erenay; Yuret, Deniz; Kanerva, Jenna; Ginter, Filip; Miekka, Niko; Leino, Akseli; Salakoski, Tapio; Lim, KyungTae; Park, Cheoneum; Lee, Changki; Poibeau, Thierry; Bhat, Riyaz Ahmad; Bhat, Irshad; Bangalore, Srinivas; Qi, Peng; Dozat, Timothy; Zhang, Yuhao; Manning, Christopher; Boroș, Tiberiu; Dumitrescu, Stefan Daniel; Burtica, Ruxandra; Arakelyan, Gor; Hambardzumyan, Karen; Khachatrian, Hrant; Rosa, Rudolf; Mareček, David; Straka, Milan; Seker, Amit; More, Amir; Tsarfaty, Reut; Önder, Berkay Furkan; Gümeli, Can; Jawahar, Ganesh; Muller, Benjamin; Fethi, Amal; Martin, Louis; Villemonte de la Clergerie, Eric; Sagot, Benoît; Seddah, Djamé; Özateş, Şaziye Betül; Özgür, Arzucan; Gungor, Tunga; Öztürk, Balkız; Ji, Tao; Liu, Yufang; Wang, Yijun; Wu, Yuanbin; Lan, Man; Chen, Danlu; Lin, Mengxiao; Hu, Zhifeng; Qiu, Xipeng. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018
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Abstract:
Test data parsed by systems submitted to the CoNLL 2018 UD parsing shared task.
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Keyword:
conllu; parsed data; universal dependencies
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URL: http://hdl.handle.net/11234/1-2885
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Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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