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JW300: A wide-coverage parallel corpus for low-resource languages
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Agic, Ž; Vulic, Ivan. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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Specializing distributional vectors of all words for lexical entailment
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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Cross-lingual semantic specialization via lexical relation induction
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Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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Informing unsupervised pretraining with external linguistic knowledge
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Do we really need fully unsupervised cross-lingual embeddings?
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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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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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On the Limitations of Unsupervised Bilingual Dictionary Induction ...
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Scoring Lexical Entailment with a Supervised Directional Similarity Network ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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