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Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer
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In: Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.inria.fr/hal-01798801 ; Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan (2018)
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)
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Benchmarking Joint Lexical and Syntactic Analysis on Multiword-Rich Data
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In: Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017) ; MWE 2017 - 13th Workshop on Multiword Expressions ; https://hal.inria.fr/hal-01677416 ; MWE 2017 - 13th Workshop on Multiword Expressions, Apr 2017, Valencia, Spain. pp.181 - 186 (2017)
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Improving neural tagging with lexical information
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In: 15th International Conference on Parsing Technologies ; https://hal.inria.fr/hal-01592055 ; 15th International Conference on Parsing Technologies, Sep 2017, Pisa, Italy. pp.25-31 ; http://compling.ucdavis.edu/iwpt2017/ (2017)
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15 |
Universal Dependencies 2.1
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In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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Annotating omission in statement pairs
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In: 11th Linguistic Annotation Workshop ; https://hal.inria.fr/hal-01584035 ; 11th Linguistic Annotation Workshop, Apr 2017, Valencia, Spain. pp.41-45 (2017)
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When is multitask learning effective? Semantic sequence prediction under varying data conditions
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In: EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01677427 ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.1-10 ; http://eacl2017.org/ (2017)
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Abstract:
International audience ; Multitask learning has been applied successfully to a range of tasks, mostly mor-phosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-01677427/file/eacl2017.pdf https://hal.inria.fr/hal-01677427 https://hal.inria.fr/hal-01677427/document
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18 |
Parsing Universal Dependencies without training
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In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01677405 ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.229 - 239 ; http://eacl2017.org/ (2017)
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