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Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)
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In: https://hal.archives-ouvertes.fr/hal-03320983 ; Association for Computational Linguistics. Online, 2021 (2021)
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Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems ...
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MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models ...
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MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models ...
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What matters more: the size of the corpora or their quality? The case of automatic translation of multiword expressions using comparable corpora.
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Detecting semantic difference: a new model based on knowledge and collocational association
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In: Computational Phraseology ; 312 ; 324 (2020)
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Bridging the gap: Attending to discontinuity in identification of multiword expressions ...
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions ...
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Bridging the gap: Attending to discontinuity in identification of multiword expressions
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Rohanian, O; Taslimipoor, Shiva; Kouchaki, S. - : ACL, 2019. : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019
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Cross-lingual transfer learning and multitask learning for capturing multiword expressions
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In: 155 ; 161 (2019)
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Abstract:
This is an accepted manuscript of an article published by Association for Computational Linguistics in Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), available online: https://www.aclweb.org/anthology/W19-5119 The accepted version of the publication may differ from the final published version. ; Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems. In this study, we explore for the first time, the application of transfer learning (TRL) and multitask learning (MTL) to the identification of Multiword Expressions (MWEs). For MTL, we exploit the shared syntactic information between MWE and dependency parsing models to jointly train a single model on both tasks. We specifically predict two types of labels: MWE and dependency parse. Our neural MTL architecture utilises the supervision of dependency parsing in lower layers and predicts MWE tags in upper layers. In the TRL scenario, we overcome the scarcity of data by learning a model on a larger MWE dataset and transferring the knowledge to a resource-poor setting in another language. In both scenarios, the resulting models achieved higher performance compared to standard neural approaches.
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Keyword:
multitask learning; Multiword Expressions; transfer learning
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URL: http://hdl.handle.net/2436/623211 https://doi.org/10.18653/v1/W19-5119
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Bridging the gap: attending to discontinuity in identification of multiword expressions
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GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
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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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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop
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In: Language Science Press; (2018)
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