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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 – 9 of 9 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Enhancing Cognitive Models of Emotions with Representation Learning ... Guo, Yuting; Choi, Jinho. - : arXiv, 2021 BASE Show details 5 Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation ... Xu, Liyan; Zhang, Xuchao; Zhao, Xujiang. - : arXiv, 2021 BASE Show details 6 Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope ... Williamson, Gregor; Elliott, Patrick; Ji, Yuxin. - : arXiv, 2021 BASE Show details 7 The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Choi, Jinho; He, Han. - : Underline Science Inc., 2021 BASE Show details 8 Levi Graph AMR Parser using Heterogeneous Attention ... 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; Choi, Jinho; He, Han. - : Underline Science Inc., 2021 Abstract: Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing. Many prior works, however, rely on the biaffine decoder for either or both arc and label predictions although most features used by the decoder may be learned by the transformer already. This paper presents a novel approach to AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one input to a transformer to learn attention, and use only attention matrices from the transformer to predict all elements in AMR graphs (concepts, arcs, labels). Although our models use significantly fewer parameters than the previous state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0 and 3.0. ... Keyword: Computational Linguistics; Condensed Matter Physics; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/avnb-mw27 https://underline.io/lecture/29933-levi-graph-amr-parser-using-heterogeneous-attention BASE Hide details 9 Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph ... Xu, Liyan; Zhang, Xuchao; Zong, Bo. - : arXiv, 2021 BASE Show details

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