Vassilis Papavassiliou


2022

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Constructing Parallel Corpora from COVID-19 News using MediSys Metadata
Dimitrios Roussis | Vassilis Papavassiliou | Sokratis Sofianopoulos | Prokopis Prokopidis | Stelios Piperidis
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents a collection of parallel corpora generated by exploiting the COVID-19 related dataset of metadata created with the Europe Media Monitor (EMM) / Medical Information System (MediSys) processing chain of news articles. We describe how we constructed comparable monolingual corpora of news articles related to the current pandemic and used them to mine about 11.2 million segment alignments in 26 EN-X language pairs, covering most official EU languages plus Albanian, Arabic, Icelandic, Macedonian, and Norwegian. Subsets of this collection have been used in shared tasks (e.g. Multilingual Semantic Search, Machine Translation) aimed at accelerating the creation of resources and tools needed to facilitate access to information in the COVID-19 emergency situation.

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SciPar: A Collection of Parallel Corpora from Scientific Abstracts
Dimitrios Roussis | Vassilis Papavassiliou | Prokopis Prokopidis | Stelios Piperidis | Vassilis Katsouros
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents SciPar, a new collection of parallel corpora created from openly available metadata of bachelor theses, master theses and doctoral dissertations hosted in institutional repositories, digital libraries of universities and national archives. We describe first how we harvested and processed metadata from 86, mainly European, repositories to extract bilingual titles and abstracts, and then how we mined high quality sentence pairs in a wide range of scientific areas and sub-disciplines. In total, the resource includes 9.17 million segment alignments in 31 language pairs and is publicly available via the ELRC-SHARE repository. The bilingual corpora in this collection could prove valuable in various applications, such as cross-lingual plagiarism detection or adapting Machine Translation systems for the translation of scientific texts and academic writing in general, especially for language pairs which include English.

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The ARC-NKUA Submission for the English-Ukrainian General Machine Translation Shared Task at WMT22
Dimitrios Roussis | Vassilis Papavassiliou
Proceedings of the Seventh Conference on Machine Translation (WMT)

The ARC-NKUA (“Athena” Research Center - National and Kapodistrian University of Athens) submission to the WMT22 General Machine Translation shared task concerns the unconstrained tracks of the English-Ukrainian and Ukrainian-English translation directions. The two Neural Machine Translation systems are based on Transformer models and our primary submissions were determined through experimentation with (a) ensemble decoding, (b) selected fine-tuning with a subset of the training data, (c) data augmentation with back-translated monolingual data, and (d) post-processing of the translation outputs. Furthermore, we discuss filtering techniques and the acquisition of additional data used for training the systems.

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Signing Avatar Performance Evaluation within EASIER Project
Athanasia-Lida Dimou | Vassilis Papavassiliou | John McDonald | Theodore Goulas | Kyriaki Vasilaki | Anna Vacalopoulou | Stavroula-Evita Fotinea | Eleni Efthimiou | Rosalee Wolfe
Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

The direct involvement of deaf users in the development and evaluation of signing avatars is imperative to achieve legibility and raise trust among synthetic signing technology consumers. A paradigm of constructive cooperation between researchers and the deaf community is the EASIER project , where user driven design and technology development have already started producing results. One major goal of the project is the direct involvement of sign language (SL) users at every stage of development of the project’s signing avatar. As developers wished to consider every parameter of SL articulation including affect and prosody in developing the EASIER SL representation engine, it was necessary to develop a steady communication channel with a wide public of SL users who may act as evaluators and can provide guidance throughout research steps, both during the project’s end-user evaluation cycles and beyond. To this end, we have developed a questionnaire-based methodology, which enables researchers to reach signers of different SL communities on-line and collect their guidance and preferences on all aspects of SL avatar animation that are under study. In this paper, we report on the methodology behind the application of the EASIER evaluation framework for end-user guidance in signing avatar development as it is planned to address signers of four SLs -Greek Sign Language (GSL), French Sign Language (LSF), German Sign Language (DGS) and Swiss German Sign Language (DSGS)- during the first project evaluation cycle. We also briefly report on some interesting findings from the pilot implementation of the questionnaire with content from the Greek Sign Language (GSL).

2018

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The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task
Vassilis Papavassiliou | Sokratis Sofianopoulos | Prokopis Prokopidis | Stelios Piperidis
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submission of the Institute for Language and Speech Processing/Athena Research and Innovation Center (ILSP/ARC) for the WMT 2018 Parallel Corpus Filtering shared task. We explore several properties of sentences and sentence pairs that our system explored in the context of the task with the purpose of clustering sentence pairs according to their appropriateness in training MT systems. We also discuss alternative methods for ranking the sentence pairs of the most appropriate clusters with the aim of generating the two datasets (of 10 and 100 million words as required in the task) that were evaluated. By summarizing the results of several experiments that were carried out by the organizers during the evaluation phase, our submission achieved an average BLEU score of 26.41, even though it does not make use of any language-specific resources like bilingual lexica, monolingual corpora, or MT output, while the average score of the best participant system was 27.91.

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Discovering Parallel Language Resources for Training MT Engines
Vassilis Papavassiliou | Prokopis Prokopidis | Stelios Piperidis
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Parallel Global Voices: a Collection of Multilingual Corpora with Citizen Media Stories
Prokopis Prokopidis | Vassilis Papavassiliou | Stelios Piperidis
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a new collection of multilingual corpora automatically created from the content available in the Global Voices websites, where volunteers have been posting and translating citizen media stories since 2004. We describe how we crawled and processed this content to generate parallel resources comprising 302.6K document pairs and 8.36M segment alignments in 756 language pairs. For some language pairs, the segment alignments in this resource are the first open examples of their kind. In an initial use of this resource, we discuss how a set of document pair detection algorithms performs on the Greek-English corpus.

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The ILSP/ARC submission to the WMT 2016 Bilingual Document Alignment Shared Task
Vassilis Papavassiliou | Prokopis Prokopidis | Stelios Piperidis
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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Abu-MaTran at WMT 2015 Translation Task: Morphological Segmentation and Web Crawling
Raphael Rubino | Tommi Pirinen | Miquel Esplà-Gomis | Nikola Ljubešić | Sergio Ortiz-Rojas | Vassilis Papavassiliou | Prokopis Prokopidis | Antonio Toral
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Abu-MaTran: Automatic building of Machine Translation
Antonio Toral | Tommi A. Pirinen | Andy Way | Gema Ramírez-Sánchez | Sergio Ortiz Rojas | Raphael Rubino | Miquel Esplà | Mikel L. Forcada | Vassilis Papavassiliou | Prokopis Prokopidis | Nikola Ljubešić
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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Abu-MaTran: Automatic building of Machine Translation
Antonio Toral | Tommi A Pirinen | Andy Way | Gema Ramírez-Sánchez | Sergio Ortiz Rojas | Raphael Rubino | Miquel Esplà | Mikel Forcada | Vassilis Papavassiliou | Prokopis Prokopidis | Nikola Ljubešić
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

2014

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Comparing two acquisition systems for automatically building an English—Croatian parallel corpus from multilingual websites
Miquel Esplà-Gomis | Filip Klubička | Nikola Ljubešić | Sergio Ortiz-Rojas | Vassilis Papavassiliou | Prokopis Prokopidis
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we compare two tools for automatically harvesting bitexts from multilingual websites: bitextor and ILSP-FC. We used both tools for crawling 21 multilingual websites from the tourism domain to build a domain-specific English―Croatian parallel corpus. Different settings were tried for both tools and 10,662 unique document pairs were obtained. A sample of about 10% of them was manually examined and the success rate was computed on the collection of pairs of documents detected by each setting. We compare the performance of the settings and the amount of different corpora detected by each setting. In addition, we describe the resource obtained, both by the settings and through the human evaluation, which has been released as a high-quality parallel corpus.

2013

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A modular open-source focused crawler for mining monolingual and bilingual corpora from the web
Vassilis Papavassiliou | Prokopis Prokopidis | Gregor Thurmair
Proceedings of the Sixth Workshop on Building and Using Comparable Corpora

2012

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Domain Adaptation of Statistical Machine Translation using Web-Crawled Resources: A Case Study
Pavel Pecina | Antonio Toral | Vassilis Papavassiliou | Prokopis Prokopidis | Josef van Genabith
Proceedings of the 16th Annual Conference of the European Association for Machine Translation

2011

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Towards Using Web-Crawled Data for Domain Adaptation in Statistical Machine Translation
Pavel Pecina | Antonio Toral | Andy Way | Vassilis Papavassiliou | Prokopis Prokopidis | Maria Giagkou
Proceedings of the 15th Annual Conference of the European Association for Machine Translation