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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 – 15 of 15 1 Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ... Ribeiro, Leonardo F. R.; Pfeiffer, Jonas; Zhang, Yue. - : arXiv, 2021 BASE Show details 2 TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing ... Gui, Tao; Wang, Xiao; Zhang, Qi. - : arXiv, 2021 BASE Show details 3 Investigating Non-local Features for Neural Constituency Parsing ... Cui, Leyang; Yang, Sen; Zhang, Yue. - : arXiv, 2021 BASE Show details 4 Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cui, Leyang; Liu, Shujie. - : Underline Science Inc., 2021 BASE Show details 5 Natural Language Processing Meets Quantum Physics: A Survey and Categorization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Li, Jian; Wu, Sixuan. - : Underline Science Inc., 2021 BASE Show details 6 Entity Relation Extraction as Dependency Parsing in Visually Rich Documents ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Zhang, Yue. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.218/ Abstract: Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation. The results demonstrate that our proposed model achieves 65.96% F1 ... Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/wb4w-rr65 https://underline.io/lecture/38076-entity-relation-extraction-as-dependency-parsing-in-visually-rich-documents BASE Hide details 7 G-Transformer for Document-Level 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; ., Boxing; Bao, Guangsheng. - : Underline Science Inc., 2021 BASE Show details 8 Dialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset ... 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; Chen, Yulong; Chen, Liang. - : Underline Science Inc., 2021 BASE Show details 9 Green NLP panel ... 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; ., Iryna; Arase, Yuki. - : Underline Science Inc., 2021 BASE Show details 10 Semantic Representation for Dialogue Modeling ... 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; Bai, Xuefeng; Chen, Yulong. - : Underline Science Inc., 2021 BASE Show details 11 Generalized Supervised Attention for Text Generation ... 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; Jiang, Yong; Liu, Yixian. - : Underline Science Inc., 2021 BASE Show details 12 Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; Pfeiffer, Jonas. - : Underline Science Inc., 2021 BASE Show details 13 Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter ... Liu, Wei; Fu, Xiyan; Zhang, Yue. - : arXiv, 2021 BASE Show details 14 Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter ... 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; Fu, Xiyan; Liu, Wei. - : Underline Science Inc., 2021 BASE Show details 15 Variations in the Home Language Environment and Early Language Development in Rural China Ma, Yue; Jonsson, Laura; Feng, Tianli... In: Int J Environ Res Public Health (2021) BASE Show details

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