Results of the Translation Inference Across Dictionaries 2019 Shared Task

Gracia, Jorge; Kabashi, Besim; Kernerman, Ilan; Lanau-Coronas, Marta; Lonke, Dorielle

doi:10.5281/zenodo.3555155

Cited by 1 publication

(4 citation statements)

References 6 publications

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“…A number of works, based on graph exploration, word embeddings, parallel corpora, etc. have participated so far in the campaign to test their ideas, many of them preliminary and subject to continuous improvement [12,19,27].…”

Section: Systematic Evaluationmentioning

confidence: 99%

“…As the TIAD campaign has showed, the OTIC algorithm continues to be a powerful method for translation inference, and it has proven to be very effective even in comparison with more contemporary methods [12,19]. However, OTIC needs a pivot language to operate, while cycle-based systems can discover translations between more indirectly connected languages.…”

Section: Comparison With Oticmentioning

confidence: 99%

“…11 https://tiad2021.unizar.es/results.html 12 The Apertium RDF data dumps, developed by Goethe University Frankfurt, are available in Zenodo through this URL: https://tinyurl.com/ apertiumrdfv2. More details on the generation of Apertium RDF v2 can be found at [11].…”

Section: Translation Graphmentioning

confidence: 99%

“…While a high BWP is maintained across all language pairs, the prediction set size varies greatly based on the richness of Apertium RDF entries in the input. Some of the language pairs with the lowest number of translations produced are: por-glg (6,044), spa-por (10,176), eus-spa (12,243). On the other hand, language pairs with the highest number of translations produced are: spa-cat (50,145), eng-cat (52,323), fra-cat (68,087).…”

Section: Dictionary Generationmentioning

confidence: 99%

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Bilingual dictionary generation and enrichment via graph exploration

Goel

Gracia

Forcada

2022

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In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.

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Section: Systematic Evaluationmentioning

confidence: 99%

Section: Comparison With Oticmentioning

confidence: 99%