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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 – 7 of 7 1 Variance-Aware Machine Translation Test Sets ... Zhan, Runzhe; Liu, Xuebo; Wong, Derek F.. - : arXiv, 2021 BASE Show details 2 Difficulty-Aware Machine Translation Evaluation ... 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; Chao, Lidia S.; Liu, Xuebo. - : Underline Science Inc., 2021 BASE Show details 3 On the Copying Behaviors of Pre-Training for Neural Machine Translation ... 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; Chao, Lidia S.; Ding, Liang. - : Underline Science Inc., 2021 BASE Show details 4 AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting ... Wang, Wenhai; Liu, Xuebo; Ji, Xiaozhong; Xie, Enze; Liang, Ding; Yang, Zhibo; Lu, Tong; Shen, Chunhua; Luo, Ping. - : arXiv, 2020 Abstract: Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when the spacing between characters is large or the characters are evenly spread in multiple rows and columns, making many visually plausible groupings of the characters (e.g. "BERLIN" is incorrectly detected as "BERL" and "IN" in Fig. 1(c)). Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection. The proposed AE TextSpotter has three important benefits. 1) The linguistic representation is learned together with the visual representation in a framework. To our knowledge, it is the first time to improve text detection by using a language model. 2) A carefully designed language module is utilized to reduce the ... : Accepted by ECCV 2020 ... Keyword: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://arxiv.org/abs/2008.00714 https://dx.doi.org/10.48550/arxiv.2008.00714 BASE Hide details 5 Norm-Based Curriculum Learning for Neural Machine Translation ... Liu, Xuebo; Lai, Houtim; Wong, Derek F.. - : arXiv, 2020 BASE Show details 6 Understanding and Improving Lexical Choice in Non-Autoregressive Translation ... Ding, Liang; Wang, Longyue; Liu, Xuebo. - : arXiv, 2020 BASE Show details 7 Shared-Private Bilingual Word Embeddings for Neural Machine Translation ... Liu, Xuebo; Wong, Derek F.; Liu, Yang. - : arXiv, 2019 BASE Show details

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