Esaú Villatoro-Tello

Also published as: Esau Villatoro-Tello


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Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)
Shantipriya Parida | Subhadarshi Panda | Amulya Dash | Esau Villatoro-Tello | A. Seza Doğruöz | Rosa M. Ortega-Mendoza | Amadeo Hernández | Yashvardhan Sharma | Petr Motlicek
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri”, “Spanish-Asháninka”, and “Spanish-Rarámuri” in the category “Development set not used for training”. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.


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BertAA : BERT fine-tuning for Authorship Attribution
Maël Fabien | Esau Villatoro-Tello | Petr Motlicek | Shantipriya Parida
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

Identifying the author of a given text can be useful in historical literature, plagiarism detection, or police investigations. Authorship Attribution (AA) has been well studied and mostly relies on a large feature engineering work. More recently, deep learning-based approaches have been explored for Authorship Attribution (AA). In this paper, we introduce BertAA, a fine-tuning of a pre-trained BERT language model with an additional dense layer and a softmax activation to perform authorship classification. This approach reaches competitive performances on Enron Email, Blog Authorship, and IMDb (and IMDb62) datasets, up to 5.3% (relative) above current state-of-the-art approaches. We performed an exhaustive analysis allowing to identify the strengths and weaknesses of the proposed method. In addition, we evaluate the impact of including additional features (e.g. stylometric and hybrid features) in an ensemble approach, improving the macro-averaged F1-Score by 2.7% (relative) on average.

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Detection of Similar Languages and Dialects Using Deep Supervised Autoencoder
Shantipriya Parida | Esau Villatoro-Tello | Sajit Kumar | Maël Fabien | Petr Motlicek
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

Language detection is considered a difficult task especially for similar languages, varieties, and dialects. With the growing number of online content in different languages, the need for reliable and robust language detection tools also increased. In this work, we use supervised autoencoders with a bayesian optimizer for language detection and highlights its efficiency in detecting similar languages with dialect variance in comparison to other state-of-the-art techniques. We evaluated our approach on multiple datasets (Ling10, Discriminating between Similar Language (DSL), and Indo-Aryan Language Identification (ILI)). Obtained results demonstrate that SAE are higly effective in detecting languages, up to a 100% accuracy in the Ling10. Similarly, we obtain a competitive performance in identifying similar languages, and dialects, 92% and 85% for DSL ans ILI datasets respectively.


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Sexual predator detection in chats with chained classifiers
Hugo Jair Escalante | Esaú Villatoro-Tello | Antonio Juárez | Manuel Montes-y-Gómez | Luis Villaseñor
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis