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TIAD 2019 Shared Task: Leveraging knowledge graphs with neural machine translation for automatic multilingual dictionary generation
(National University of Ireland, Galway, 2019-04-20)
This paper describes the different proposed approaches to the TIAD 2019 Shared Task, which consisted in the automatic discovery and generation of dictionaries leveraging multilingual knowledge bases. We present three methods ...
Leveraging rule-based machine translation knowledge for under-resourced neural machine translation models
(NUI Galway, 2019-08-19)
Rule-based machine translation is a machine translation paradigm where linguistic
knowledge is encoded by an expert in the
form of rules that translate from source to
target language. While this approach grants
total ...
Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation
(National University of Ireland, Galway, 2019-05-20)
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 ...
Multilingual multimodal machine translation for Dravidian languages utilizing phonetic transcription
(European Association for Machine Translation, 2019-08-19)
Multimodal machine translation is the task
of translating from a source text into the
target language using information from
other modalities. Existing multimodal
datasets have been restricted to only highly
resourced ...
WordNet gloss translation for under-resourced languages using multilingual neural machine translation
(European Association for Machine Translation, 2019-08-19)
In this paper, we translate the glosses in
the English WordNet based on the expand approach for improving and generating wordnets with the help of multilingual
neural machine translation. Neural Machine Translation (NMT) ...
Lexical sense alignment using weighted bipartite b-matching
(NUI Galway, 2019-05-20)
In this study, we present a similarity-based approach for lexical sense alignment in WordNet and Wiktionary with a focus on the polysemous items. Our approach relies on semantic textual similarity using features such as ...