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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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Abstract:
Under-resourced languages are a significant challenge for statistical approaches to machine translation, and recently it has been shown that the usage of training data from closely-related languages can improve machine translation quality of these languages. While languages within the same language family share many properties, many under-resourced languages are written in their own native script, which makes taking advantage of these language similarities difficult. In this paper, we propose to alleviate the problem of different scripts by transcribing the native script into common representation i.e. the Latin script or the International Phonetic Alphabet (IPA). In particular, we compare the difference between coarse-grained transliteration to the Latin script and fine-grained IPA transliteration. We performed experiments on the language pairs English-Tamil, English-Telugu, and English-Kannada translation task. Our results show improvements in terms of the BLEU, METEOR and chrF scores from transliteration and we find that the transliteration into the Latin script outperforms the fine-grained IPA transcription.
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Keyword:
Data processing Computer science; Dravidian languages; International Phonetic Alphabet; IPA; M; Machine translation; Phonetic transcription; Transliteration; Under-resourced languages
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URN:
urn:nbn:de:0030-drops-103700
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URL: https://drops.dagstuhl.de/opus/volltexte/2019/10370/ https://doi.org/10.4230/OASIcs.LDK.2019.6
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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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