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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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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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