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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 – 5 of 5 1 Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data ... 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; Cao, Yanshuai; Norouzi, Sajad. - : Underline Science Inc., 2021 BASE Show details 2 Hierarchical Neural Data Synthesis for Semantic Parsing ... Yang, Wei; Xu, Peng; Cao, Yanshuai. - : arXiv, 2021 BASE Show details 3 Optimizing Deeper Transformers on Small Datasets ... 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; Cao, Yanshuai; Cheung, Jackie Chi Kit; Huang , Chenyang; Kumar, Dhruv; Prince, Simon; Tang, Keyi; Xu, Peng; Yang, Wei; Zi, Wenjie. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.163 Abstract: It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train 48 layers of transformers, comprising 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state of the art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data dependent Transformer Fixed-update initialization scheme ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/ehsy-3055 https://underline.io/lecture/25482-optimizing-deeper-transformers-on-small-datasets BASE Hide details 4 TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface ... 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; Alon, Meidan; Ateeq, Jawad. - : Underline Science Inc., 2021 BASE Show details 5 Adversarial Contrastive Estimation ... Bose, Avishek Joey; Ling, Huan; Cao, Yanshuai. - : arXiv, 2018 BASE Show details

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