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20 results on '"Chinea-Rios, Mara"'

1. Zero and Few-shot Learning for Author Profiling

Author

Chinea-Rios, Mara, Müller, Thomas, Sarracén, Gretel Liz De la Peña, Rangel, Francisco, and Franco-Salvador, Marc

Subjects
Computer Science - Computation and Language
Abstract

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.

Published
2022

2. Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection : Condensed Lab Overview

Author

Bevendorff, Janek, Borrego-Obrador, Ian, Chinea-Ríos, Mara, Franco-Salvador, Marc, Fröbe, Maik, Heini, Annina, Kredens, Krzysztof, Mayerl, Maximilian, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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3. Overview of PAN 2023: Authorship Verification, Multi-author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection : Extended Abstract

Author

Bevendorff, Janek, Chinea-Ríos, Mara, Franco-Salvador, Marc, Heini, Annina, Körner, Erik, Kredens, Krzysztof, Mayerl, Maximilian, Pęzik, Piotr, Potthast, Martin, Rangel, Francisco, Rosso, Paolo, Stamatatos, Efstathios, Stein, Benno, Wiegmann, Matti, Wolska, Magdalena, Zangerle, Eva, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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4. Neural Networks Classifier for Data Selection in Statistical Machine Translation

Author

Peris, Álvaro, Chinea-Rios, Mara, and Casacuberta, Francisco

Subjects
Computer Science - Computation and Language
Abstract

We address the data selection problem in statistical machine translation (SMT) as a classification task. The new data selection method is based on a neural network classifier. We present a new method description and empirical results proving that our data selection method provides better translation quality, compared to a state-of-the-art method (i.e., Cross entropy). Moreover, the empirical results reported are coherent across different language pairs., Comment: Submitted to EACL'17

Published
2016

7. Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation

Author

Chinea-Rios, Mara, Sanchis-Trilles, Germán, Casacuberta, Francisco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Alexandre, Luís A., editor, Salvador Sánchez, José, editor, and Rodrigues, João M. F., editor

Published
2017
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9. Bilingual Data Selection Using a Continuous Vector-Space Representation

Author

Chinea-Rios, Mara, Sanchis-Trilles, Germán, Casacuberta, Francisco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Robles-Kelly, Antonio, editor, Loog, Marco, editor, Biggio, Battista, editor, Escolano, Francisco, editor, and Wilson, Richard, editor

Published
2016
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10. Sentence Clustering Using Continuous Vector Space Representation

Author

Chinea-Rios, Mara, Sanchis-Trilles, Germán, Casacuberta, Francisco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Paredes, Roberto, editor, Cardoso, Jaime S., editor, and Pardo, Xosé M., editor

Published
2015
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13. Vector sentences representation for data selection in statistical machine translation

Author

Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Generalitat Valenciana, Universitat Politècnica de València, Chinea-Rios, Mara, Sanchis Trilles, Germán, Casacuberta Nolla, Francisco, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Generalitat Valenciana, Universitat Politècnica de València, Chinea-Rios, Mara, Sanchis Trilles, Germán, and Casacuberta Nolla, Francisco

Abstract

[EN] One of the most popular approaches to machine translation consists in formulating the problem as a pattern recognition approach. Under this perspective, bilingual corpora are precious resources, as they allow for a proper estimation of the underlying models. In this framework, selecting the best possible corpus is critical, and data selection aims to find the best subset of the bilingual sentences from an available pool of sentences such that the final translation quality is improved. In this paper, we present a new data selection technique that leverages a continuous vector-space representation of sentences. Experimental results report improvements compared not only with a system trained only with in-domain data, but also compared with a system trained on all the available data. Finally, we compared our proposal with other state-of-the-art data selection techniques (Cross-entropy selection and Infrequent ngrams recovery) in two different scenarios, obtaining very promising results with our proposal: our data selection strategy is able to yield results that are at least as good as the best-performing strfategy for each scenario. The empirical results reported are coherent across different language pairs.

Published
2019

14. Are Automatic Metrics Robust and Reliable in Specific Machine Translation Tasks?

Author

Chinea-Rios, Mara, Peris, Álvaro, and Casacuberta, Francisco

Subjects
Machine Translation, Lenguajes y Sistemas Informáticos
Abstract

We present a comparison of automatic metrics against human evaluations of translation quality in several scenarios which were unexplored up to now. Our experimentation was conducted on translation hypotheses that were problematic for the automatic metrics, as the results greatly diverged from one metric to another. We also compared three different translation technologies. Our evaluation shows that in most cases, the metrics capture the human criteria. However, we face failures of the automatic metrics when applied to some domains and systems. Interestingly, we find that automatic metrics applied to the neural machine translation hypotheses provide the most reliable results. Finally, we provide some advice when dealing with these problematic domains. The research leading to these results has received funding from the Generalitat Valenciana under grant PROMETEO/2018/004.

Published
2018

15. Creating the best development corpus for Statistical Machine Translation systems

Author

Chinea-Rios, Mara, Sanchis-Trilles, Germán, and Casacuberta, Francisco

Subjects
Machine Translation, Lenguajes y Sistemas Informáticos
Abstract

We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions. The research leading to these results were partially supported by projects CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER) and PROMETEO/2018/004.

Published
2018

20. An Empirical Analysis of Data Selection Techniques in Statistical Machine Translation

Author

Chinea-Rios, Mara, Sanchis-Triches, Germán, Casacuberta Nolla, Francisco, Chinea-Rios, Mara, Sanchis-Triches, Germán, and Casacuberta Nolla, Francisco

Abstract

Domain adaptation has recently gained interest in statistical machine translation. One of the adaptation techniques is based in the selection data. Data selection aims to select the best subset of the bilingual sentences from an available pool of sentences, with which to train a SMT system. In this paper, we study how affect the bilingual corpora used for the data selection methods in the translation quality., La adaptación de dominios genera mucho interés dentro de la traducción automática estadística. Una de las técnicas de adaptación está basada en la selección de datos que tiene como objetivo seleccionar el mejor subconjunto de oraciones bilingües de un gran conjunto de oraciones. En este artículo estudiamos como afectan los corpus bilingües empleados por los métodos de selección de frases en la calidad de las traducciones.

Published
2015

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