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Utilising knowledge graph embeddings for data-to-text generation
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Enhancing multiple-choice question answering with causal knowledge
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NUIG-DSI at the WebNLG+ challenge: Leveraging transfer learning for RDF-to-text generation
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Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages
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In: Chakravarthi, Bharathi Raja orcid:0000-0002-4575-7934 , Rajasekaran, Navaneethan, Arcan, Mihael orcid:0000-0002-3116-621X , McGuinness, Kevin orcid:0000-0003-1336-6477 , O'Connor, Noel E. orcid:0000-0002-4033-9135 and McCrae, John P. orcid:0000-0002-7227-1331 (2020) Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages. In: 7th Workshop on NLP for Similar Languages, Varieties and Dialects, 13 Dec 2020, Barcelona, Spain (Online). (2020)
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Leveraging orthographic information to improve machine translation of under-resourced languages
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NUIG at TIAD: Combining unsupervised NLP and graph metrics for translation inference
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Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios ...
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Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages ...
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Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages ...
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Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages
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TIAD 2019 Shared Task: Leveraging knowledge graphs with neural machine translation for automatic multilingual dictionary generation
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The ESSOT system goes wild: an easy way for translating ontologies
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Generating linked-data based domain-specific sentiment lexicons from legacy language and semantic resources
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Automatic enrichment of terminological resources: the IATE RDF example
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Avtomatsko pridobivanje besednih zvez iz korpusa z uporabo leksikona SSJ
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Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation
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Abstract:
In the widely-connected digital world, multilingual lexical resources are one of the most important resources, for natural language processing applications, including information retrieval, question answering or knowledge management. These applications benefit from the multilingual knowledge as well as from the semantic relation between the words documented in these resources. Since multilingual dictionary creation and curation is a time-consuming task, we explored the use of multi-way neural machine translation trained on corpora of languages from the same family and trained additionally with a relatively small human-validated dictionary to infer new translation candidates. Our results showed not only that new dictionary entries can be identified and extracted from the translation model, but also that the expected precision and recall of the resulting dictionary can be adjusted by using different thresholds. ; This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund, and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731015, ELEXIS - European Lexical Infrastructure. ; peer-reviewed ; 2019-05-20
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Keyword:
Automatic inference; Dictionary generation; Neural machine translation
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URL: http://hdl.handle.net/10379/15140
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