Papers by Katia Lida Kermanidis
Improving Machine Translation of Educational Content via Crowdsourcing (L18-1)
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Maximiliana Behnke, Antonio Valerio Miceli Barone, Rico Sennrich, Vilelmini Sosoni, Thanasis Naskos, Eirini Takoulidou, Maria Stasimioti, Menno van Zaanen, Sheila Castilho, Federico Gaspari, Panayota Georgakopoulou, Valia Kordoni, Markus Egg, Katia Lida Kermanidis
| Challenge: | Using crowdsourcing to train neural machine translation models is expensive and expensive . professional outsourcing of bilingual data is expensive if the translations are of a lower quality . |
| Approach: | They analyze the impact of crowdsourcing on the quality of in-domain training data . they use translations of MOOCs from English to eleven languages to fine-tune machine translation models . |
| Outcome: | The proposed method improves on general-domain training data and with pre-existing in-domain corpora. |
Translation Crowdsourcing: Creating a Multilingual Corpus of Online Educational Content (L18-1)
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Vilelmini Sosoni, Katia Lida Kermanidis, Maria Stasimioti, Thanasis Naskos, Eirini Takoulidou, Menno van Zaanen, Sheila Castilho, Panayota Georgakopoulou, Valia Kordoni, Markus Egg
| Challenge: | a large corpus of online content has been developed via large-scale crowdsourcing. |
| Approach: | They describe a multilingual corpus of online content that has been manually translated into 11 European and BRIC languages using the crowdsourcing platform. |
| Outcome: | The proposed corpus is a product of the EU-funded TraMOOC project and is used to train, tune and test machine translation engines. |
A Multilingual Wikified Data Set of Educational Material (L18-1)
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Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal van den Bosch
| Challenge: | a crowdsourcing effort to annotate and link parallel texts has been unsuccessful . a data set of parallel texts in eleven languages is presented . |
| Approach: | They present a wikified data set of English sentences linked to Wikipedia pages . they use crowdsourcing to annotate the texts and perform crowdsourcing for complex annotations . |
| Outcome: | The proposed data set is valuable as it constitutes a rich resource . it includes annotated data of English sentences linked to translations in eleven languages . |
A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web (2020.lrec-1)
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| Challenge: | Part-of-speech tagging is a fundamental part of NLP, but it is not widely used in unstructured text processing. |
| Approach: | They propose to use part-of-speech tags to extract information from unstructured social text in Greek and a supervised part-off-seech tagger to do so. |
| Outcome: | The proposed method performs better on unstructured microblogging text than existing methods on structured text processing. |