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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 Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making ... 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; Dai, Zelin; Dong, Tiansi. - : Underline Science Inc., 2021 BASE Show details 2 CLEVE: Contrastive Pre-training for Event Extraction ... 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; Han, Xu; Hou, Lei. - : Underline Science Inc., 2021 BASE Show details 3 TWAG: A Topic-Guided Wikipedia Abstract Generator ... 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; Cui, cuitong5@huawei.com; Hou, Lei. - : Underline Science Inc., 2021 BASE Show details 4 TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cao, Shulin; Hou, Lei. - : Underline Science Inc., 2021 BASE Show details 5 Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision ... Cao, Yixin; Hou, Lei; Li, Juanzi; Liu, Zhiyuan; Li, Chengjiang; Chen, Xu; Dong, Tiansi. - : arXiv, 2018 Abstract: Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method. ... : 11 pages, EMNLP2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1811.10776 https://dx.doi.org/10.48550/arxiv.1811.10776 BASE Hide details

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