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Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining ...
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Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring ...
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Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction ...
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A Call for More Rigor in Unsupervised Cross-lingual Learning ...
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Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring ...
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Translation Artifacts in Cross-lingual Transfer Learning ...
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Learning about phraseology from corpora: A linguistically motivated approach for Multiword Expression identification
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In: PLoS One (2020)
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Adapting NMT to caption translation in Wikimedia Commons for low-resource languages
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Sarasola, Kepa orcid:0000-0003-4349-6088 , Dowling, Meghan orcid:0000-0003-1637-4923 , Way, Andy orcid:0000-0001-5736-5930 , Labaka, Gorka orcid:0000-0003-4611-2502 and Alegria, Iñaki orcid:0000-0002-0272-1472 (2019) Adapting NMT to caption translation in Wikimedia Commons for low-resource languages. Procesamiento de Lenguaje Natural, 63 . pp. 33-40. ISSN 1135-5948 (2019)
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Adapting NMT to caption translation in Wikimedia Commons for low-resource languages
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Sarasola, Kepa orcid:0000-0003-4349-6088 , Dowling, Meghan orcid:0000-0003-1637-4923 , Way, Andy orcid:0000-0001-5736-5930 , Labaka, Gorka orcid:0000-0003-4611-2502 and Alegria, Iñaki orcid:0000-0002-0272-1472 (2019) Adapting NMT to caption translation in Wikimedia Commons for low-resource languages. Procesamiento del Lenguaje Natural, 63 . pp. 33-40. ISSN 1135-5948 (2019)
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Analyzing the Limitations of Cross-lingual Word Embedding Mappings ...
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Neural machine translation of clinical texts between long distance languages
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In: J Am Med Inform Assoc (2019)
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Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation ...
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Abstract:
Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness. In this paper, we show that each embedding model captures more information than directly apparent. A linear transformation that adjusts the similarity order of the model without any external resource can tailor it to achieve better results in those aspects, providing a new perspective on how embeddings encode divergent linguistic information. In addition, we explore the relation between intrinsic and extrinsic evaluation, as the effect of our transformations in downstream tasks is higher for unsupervised systems than for supervised ones. ... : CoNLL 2018 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1809.02094 https://arxiv.org/abs/1809.02094
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Using linguistic data for English and Spanish verb-noun combination identification
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Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation
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In: Labaka, Gorka, Stroppa, Nicolas, Way, Andy orcid:0000-0001-5736-5930 and Sarasola, Kepa (2007) Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation. In: Machine Translation Summit XI, 10-14 September, 2007, Copenhagen, Denmark. (2007)
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