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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 – 3 of 3 1 Inducing Language-Agnostic Multilingual Representations ... 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; Zhao, Wei. - : Underline Science Inc., 2021 BASE Show details 2 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen; Kaster, Marvin. - : Underline Science Inc., 2021 BASE Show details 3 Regularized Attentive Capsule Network for Overlapped Relation Extraction ... The 28th International Conference on Computational Linguistics 2020; Jia, Weijia; Lin, Xiangyu; Liu, Tianyi; Zhao, Wei; Zhou, Mingliang. - : Underline Science Inc., 2020 Abstract: Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts. However, the automatically established training datasets in distant supervision contain low-quality instances with noisy words and overlapped relations, introducing great challenges to the accurate extraction of relations. To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlapped relations in each informal sentence. To discover multiple relation features in an instance, we embed multi-head attention into the capsule network as the low-level capsules, where the subtraction of two entities acts as a new form of relation query to select salient features regardless of their positions. To further discriminate overlapped relation features, we devise disagreement regularization to explicitly encourage the diversity among both multiple attention heads and low-level capsules. Extensive experiments ... Keyword: Computer and Information Science; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/r067-q328 https://underline.io/lecture/6133-regularized-attentive-capsule-network-for-overlapped-relation-extraction BASE Hide details

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