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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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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
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Abstract:
The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, ...
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URL: https://www.repository.cam.ac.uk/handle/1810/289394 https://dx.doi.org/10.17863/cam.36642
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain ...
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
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