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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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Abstract:
We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the ...
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Keyword:
cross-lingual vector spaces; Unsupervised cross-lingual IR
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URL: https://www.repository.cam.ac.uk/handle/1810/279400 https://dx.doi.org/10.17863/cam.26775
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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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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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