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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 – 6 of 6 1 One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers ... 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; Huang, Yongfeng; Wu, Chuhan. - : Underline Science Inc., 2021 BASE Show details 2 Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling ... 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; Huang, Yongfeng; Qi, Tao; Wu, Chuhan; Wu, Fangzhao. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.107 Abstract: Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer models documents in a hierarchical way, i.e., first learns sentence representations and then learns document representations. It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence. More specifically, we first use a sentence Transformer to learn the representations of each sentence. Then we use a document Transformer to model the global document context from these sentence representations. Next, we use another sentence Transformer to enhance sentence modeling using the global document context. Finally, we use hierarchical pooling method to obtain document ... 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/wqt9-bc64 https://underline.io/lecture/25858-hi-transformer-hierarchical-interactive-transformer-for-efficient-and-effective-long-document-modeling BASE Hide details 3 One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers ... 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; Huang, Yongfeng; Wu, Chuhan. - : Underline Science Inc., 2021 BASE Show details 4 Provably Secure Generative Linguistic Steganography ... 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; Huang, Yongfeng; Yang, Zhongliang. - : Underline Science Inc., 2021 BASE Show details 5 HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation ... 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; Huang, Yongfeng; Qi, Tao. - : Underline Science Inc., 2021 BASE Show details 6 PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity ... 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; Huang, Yongfeng; Qi, Tao. - : Underline Science Inc., 2021 BASE Show details

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