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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 Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints ... Zeng, Chun; Chen, Jiangjie; Zhuang, Tianyi. - : arXiv, 2022 BASE Show details 2 Learning the Morphological and Syntactic Grammars for Named Entity Recognition Mengtao Sun; Qiang Yang; Hao Wang; Mark Pasquine; Ibrahim A. Hameed In: Information; Volume 13; Issue 2; Pages: 49 (2022) BASE Show details 3 Factors That Prevent Mosquito-Borne Diseases among Migrant Workers in Taiwan: Application of the Health Belief Model in a Church-Based Health Promotion Study Tai, Yu-Shan; Yang, Hao-Jan In: Int J Environ Res Public Health (2022) BASE Show details 4 General Facial Representation Learning in a Visual-Linguistic Manner ... Zheng, Yinglin; Yang, Hao; Zhang, Ting. - : arXiv, 2021 BASE Show details 5 Modeling Long-distance Node Relations for KBQA with Global Dynamic Graph ... The 28th International Conference on Computational Linguistics 2020; Ao, Jianchang; Cheng , Bo; Han, Jiale; Li, Zhenzi; Wang, Xu; Yang, Hao; Zhao, Shuai. - : Underline Science Inc., 2020 Abstract: The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA). Previous studies rely on deep graph neural networks (GNNs) to capture rich structural information, which may not model node relations in particularly long-distance due to over-smoothing issue. To address this challenge, we propose a novel frameworkGlobalGraph, which models long-distance node relations from two views: 1) Node type similarity: GlobalGraph as-signs each node a global type label and models long-distance node relations through the global type label similarity; 2) Correlation between nodes and questions: we learn similarity scores be-tween nodes and the question, and model long-distance node relations through the sum score of two nodes. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our method. ... Keyword: Computer and Information Science; Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network URL: https://underline.io/lecture/8577-modeling-long-distance-node-relations-for-kbqa-with-global-dynamic-graph https://dx.doi.org/10.48448/dv0r-qp27 BASE Hide details

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