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1
Challenges and Strategies in Cross-Cultural NLP ...
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Finding Structural Knowledge in Multimodal-BERT ...
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Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning ...
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4
Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings ...
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5
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs ...
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6
On Language Models for Creoles ...
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7
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions? ...
NAACL 2021 2021; de Lhoneux, Miryam; Nivre, Joakim. - : Underline Science Inc., 2021
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I’ve got a construction looks funny – representing and recovering non-standard constructions in UD
Ruppenhofer, Josef [Verfasser]; Rehbein, Ines [Verfasser]; de Marneffe, Marie-Catherine [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2020
DNB Subject Category Language
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9
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding ...
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10
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics ...
Abstract: Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality---indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence. ... : COLING 2020 camera ready ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2011.00834
https://dx.doi.org/10.48550/arxiv.2011.00834
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11
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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12
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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13
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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14
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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15
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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17
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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18
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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19
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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20
Representation and parsing of multiword expressions: Current trends
Parmentier, Yannick; Waszczuk, Jakub; Lichte, Timm. - : Language Science Press, 2019
In: Language Science Press; (2019)
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