Páraic Sheridan


Building MT systems in low resourced languages for Public Sector users in Croatia, Iceland, Ireland, and Norway
Róisín Moran | Carla Para Escartín | Akshai Ramesh | Páraic Sheridan | Jane Dunne | Federico Gaspari | Sheila Castilho | Natalia Resende | Andy Way
Proceedings of Machine Translation Summit XVIII: Users and Providers Track

When developing Machine Translation engines, low resourced language pairs tend to be in a disadvantaged position: less available data means that developing robust MT models can be more challenging.The EU-funded PRINCIPLE project aims at overcoming this challenge for four low resourced European languages: Norwegian, Croatian, Irish and Icelandic. This presentation will give an overview of the project, with a focus on the set of Public Sector users and their use cases for which we have developed MT solutions.We will discuss the range of language resources that have been gathered through contributions from public sector collaborators, and present the extensive evaluations that have been undertaken, including significant user evaluation of MT systems across all of the public sector participants in each of the four countries involved.


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Progress of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian
Andy Way | Petra Bago | Jane Dunne | Federico Gaspari | Andre Kåsen | Gauti Kristmannsson | Helen McHugh | Jon Arild Olsen | Dana Davis Sheridan | Páraic Sheridan | John Tinsley
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

This paper updates the progress made on the PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focuses on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which have been identified as low-resource languages, especially for building effective machine translation (MT) systems. We report initial achievements of the project and ongoing activities aimed at promoting the uptake of neural MT for the low-resource languages of the project.


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Using SMT for OCR Error Correction of Historical Texts
Haithem Afli | Zhengwei Qiu | Andy Way | Páraic Sheridan
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.


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PLuTO: MT for On-Line Patent Translation
John Tinsley | Andy Way | Páraic Sheridan
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program

PLuTO – Patent Language Translation Online – is a partially EU-funded commercialization project which specializes in the automatic retrieval and translation of patent documents. At the core of the PLuTO framework is a machine translation (MT) engine through which web-based translation services are offered. The fully integrated PLuTO architecture includes a translation engine coupling MT with translation memories (TM), and a patent search and retrieval engine. In this paper, we first describe the motivating factors behind the provision of such a service. Following this, we give an overview of the PLuTO framework as a whole, with particular emphasis on the MT components, and provide a real world use case scenario in which PLuTO MT services are ex- ploited.


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Design, Construction and Validation of an Arabic-English Conceptual Interlingua for Cross-lingual Information Retrieval
Nizar Habash | Clinton Mah | Sabiha Imran | Randy Calistri-Yeh | Páraic Sheridan
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the issues involved in extending a trans-lingual lexicon, the TextWise Conceptual Interlingua (CI), with Arabic terms. The Conceptual Interlingua is based on the Princeton English WordNet (Fellbaum, 1998). It is a central component in the cross-lingual information retrieval (CLIR) system CINDOR (Conceptual INterlingua for DOcument Retrieval). Arabic has a rich morphological system combining templatic and affixational paradigms for both inflectional and derivational morphology. This rich morphology poses a major challenge to the design and building of the Arabic CI and also its validation. This is because the available resources for Arabic, whether manually constructed bilingual lexicons or lexicons automatically derived from bilingual parallel corpora, exist at different levels of morphological representation. We describe here the issues and decisions made in the design and construction of the Arabic-English CI using different types of manual and automatic resources. We also present the results of an extensive validation of the Arabic CI and briefly discuss the evaluation of its use for CLIR on the TREC Arabic Benchmark collection.