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IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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Modelling Latent Translations for Cross-Lingual Transfer ...
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Visually Grounded Reasoning across Languages and Cultures ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020)
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Probing Pretrained Language Models for Lexical Semantics ...
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Specializing unsupervised pretraining models for word-level semantic similarity
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XCOPA: A multilingual dataset for causal commonsense reasoning
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Specializing distributional vectors of all words for lexical entailment
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Cross-lingual semantic specialization via lexical relation induction
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Informing unsupervised pretraining with external linguistic knowledge
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
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Abstract:
Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doaj.org/article/e766c2c989b842e388991cd857e2c997 https://doi.org/10.1162/coli_a_00357
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