Eva Martínez Garcia

Also published as: Eva Martínez Garcia


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ELRI: A Decentralised Network of National Relay Stations to Collect, Prepare and Share Language Resources
Thierry Etchegoyhen | Borja Anza Porras | Andoni Azpeitia | Eva Martínez Garcia | José Luis Fonseca | Patricia Fonseca | Paulo Vale | Jane Dunne | Federico Gaspari | Teresa Lynn | Helen McHugh | Andy Way | Victoria Arranz | Khalid Choukri | Hervé Pusset | Alexandre Sicard | Rui Neto | Maite Melero | David Perez | António Branco | Ruben Branco | Luís Gomes
Proceedings of the 1st International Workshop on Language Technology Platforms

We describe the European Language Resource Infrastructure (ELRI), a decentralised network to help collect, prepare and share language resources. The infrastructure was developed within a project co-funded by the Connecting Europe Facility Programme of the European Union, and has been deployed in the four Member States participating in the project, namely France, Ireland, Portugal and Spain. ELRI provides sustainable and flexible means to collect and share language resources via National Relay Stations, to which members of public institutions can freely subscribe. The infrastructure includes fully automated data processing engines to facilitate the preparation, sharing and wider reuse of useful language resources that can help optimise human and automated translation services in the European Union.

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Latin-Spanish Neural Machine Translation: from the Bible to Saint Augustine
Eva Martínez Garcia | Álvaro García Tejedor
Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages

Although there are several sources where to find historical texts, they usually are available in the original language that makes them generally inaccessible. This paper presents the development of state-of-the-art Neural Machine Systems for the low-resourced Latin-Spanish language pair. First, we build a Transformer-based Machine Translation system on the Bible parallel corpus. Then, we build a comparable corpus from Saint Augustine texts and their translations. We use this corpus to study the domain adaptation case from the Bible texts to Saint Augustine’s works. Results show the difficulties of handling a low-resourced language as Latin. First, we noticed the importance of having enough data, since the systems do not achieve high BLEU scores. Regarding domain adaptation, results show how using in-domain data helps systems to achieve a better quality translation. Also, we observed that it is needed a higher amount of data to perform an effective vocabulary extension that includes in-domain vocabulary.


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Context-Aware Neural Machine Translation Decoding
Eva Martínez Garcia | Carles Creus | Cristina España-Bonet
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)

This work presents a decoding architecture that fuses the information from a neural translation model and the context semantics enclosed in a semantic space language model based on word embeddings. The method extends the beam search decoding process and therefore can be applied to any neural machine translation framework. With this, we sidestep two drawbacks of current document-level systems: (i) we do not modify the training process so there is no increment in training time, and (ii) we do not require document-level an-notated data. We analyze the impact of the fusion system approach and its parameters on the final translation quality for English–Spanish. We obtain consistent and statistically significant improvements in terms of BLEU and METEOR and we observe how the fused systems are able to handle synonyms to propose more adequate translations as well as help the system to disambiguate among several translation candidates for a word.


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Evaluating Domain Adaptation for Machine Translation Across Scenarios
Thierry Etchegoyhen | Anna Fernández Torné | Andoni Azpeitia | Eva Martínez Garcia | Anna Matamala
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech’s Participation in the WMT 2018 Quality Estimation Task
Thierry Etchegoyhen | Eva Martínez Garcia | Andoni Azpeitia
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We describe Vicomtech’s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators. The core of our approach is based on two simple features: lexical translation overlaps and language model cross-entropy scores. These features are exploited in two system variants: uMQE is an unsupervised system, where the final quality score is obtained by averaging individual feature scores; sMQE is a supervised variant, where the final score is estimated by a Support Vector Regressor trained on the available annotated datasets. The main goal of our minimalist approach to quality estimation is to provide reliable estimators that require minimal deployment effort, few resources, and, in the case of uMQE, do not depend on costly data annotation or post-editing. Our approach was applied to all language pairs in sentence quality estimation, obtaining competitive results across the board.

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STACC, OOV Density and N-gram Saturation: Vicomtech’s Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering
Andoni Azpeitia | Thierry Etchegoyhen | Eva Martínez Garcia
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We describe Vicomtech’s participation in the WMT 2018 Shared Task on parallel corpus filtering. We aimed to evaluate a simple approach to the task, which can efficiently process large volumes of data and can be easily deployed for new datasets in different language pairs and domains. We based our approach on STACC, an efficient and portable method for parallel sentence identification in comparable corpora. To address the specifics of the corpus filtering task, which features significant volumes of noisy data, the core method was expanded with a penalty based on the amount of unknown words in sentence pairs. Additionally, we experimented with a complementary data saturation method based on source sentence n-grams, with the goal of demoting parallel sentence pairs that do not contribute significant amounts of yet unobserved n-grams. Our approach requires no prior training and is highly efficient on the type of large datasets featured in the corpus filtering task. We achieved competitive results with this simple and portable method, ranking in the top half among competing systems overall.


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Weighted Set-Theoretic Alignment of Comparable Sentences
Andoni Azpeitia | Thierry Etchegoyhen | Eva Martínez Garcia
Proceedings of the 10th Workshop on Building and Using Comparable Corpora

This article presents the STACCw system for the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. The original STACC approach, based on set-theoretic operations over bags of words, had been previously shown to be efficient and portable across domains and alignment scenarios. Wedescribe an extension of this approach with a new weighting scheme and show that it provides significant improvements on the datasets provided for the shared task.


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TweetMT: A Parallel Microblog Corpus
Iñaki San Vicente | Iñaki Alegría | Cristina España-Bonet | Pablo Gamallo | Hugo Gonçalo Oliveira | Eva Martínez Garcia | Antonio Toral | Arkaitz Zubiaga | Nora Aranberri
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.


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Document-Level Machine Translation with Word Vector Models
Eva Martínez Garcia | Cristina España-Bonet | Lluís Màrquez
Proceedings of the 18th Annual Conference of the European Association for Machine Translation


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Word’s Vector Representations meet Machine Translation
Eva Martínez Garcia | Jörg Tiedemann | Cristina España-Bonet | Lluís Màrquez
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation