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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 Substructure Substitution: Structured Data Augmentation for NLP ... 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; Gimpel, Kevin; Livescu, Karen; Shi, Haoyue Freda. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.307 Abstract: We study a family of data augmentation methods, substructure substitution (SUB2), that generalizes prior methods. SUB2 generates new examples by substituting substructures (e.g., subtrees or subsequences) with others having the same label. This idea can be applied to many structured NLP tasks such as part-of-speech tagging and parsing. For more general tasks (e.g., text classification) which do not have explicitly annotated substructures, we present variations of SUB2 based on text spans or parse trees, introducing structure-aware data augmentation methods to general NLP tasks. For most cases, training with a dataset augmented by SUB2 achieves better performance than training with the original training set. Further experiments show that SUB2 has more consistent performance than other investigated augmentation methods, across different tasks and sizes of the seed dataset. ... 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/j5e6-f206 https://underline.io/lecture/26398-substructure-substitution-structured-data-augmentation-for-nlp BASE Hide details 2 Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment ... Shi, Haoyue; Zettlemoyer, Luke; Wang, Sida I.. - : arXiv, 2021 BASE Show details 3 Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment ... 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; Shi, Haoyue Freda; Wang, Sida. - : Underline Science Inc., 2021 BASE Show details 4 Deep Clustering of Text Representations for Supervision-free Probing of Syntax ... Gupta, Vikram; Shi, Haoyue; Gimpel, Kevin. - : arXiv, 2020 BASE Show details 5 Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis ... Shi, Haoyue; Sun, Yuqi; Hu, Junfeng. - : arXiv, 2018 BASE Show details 6 Learning Visually-Grounded Semantics from Contrastive Adversarial Samples ... Shi, Haoyue; Mao, Jiayuan; Xiao, Tete. - : arXiv, 2018 BASE Show details 7 Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation ... Shi, Haoyue; Li, Caihua; Hu, Junfeng. - : arXiv, 2017 BASE Show details

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