Yerai Doval

Also published as: Yerai Doval Mosquera


On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning
Yerai Doval | Jose Camacho-Collados | Luis Espinosa Anke | Steven Schockaert
Proceedings of the Twelfth Language Resources and Evaluation Conference

Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. Recent developments which construct these embeddings by aligning monolingual spaces have shown that accurate alignments can be obtained with little or no supervision, which usually comes in the form of bilingual dictionaries. However, the focus has been on a particular controlled scenario for evaluation, and there is no strong evidence on how current state-of-the-art systems would fare with noisy text or for language pairs with major linguistic differences. In this paper we present an extensive evaluation over multiple cross-lingual embedding models, analyzing their strengths and limitations with respect to different variables such as target language, training corpora and amount of supervision. Our conclusions put in doubt the view that high-quality cross-lingual embeddings can always be learned without much supervision.

Cross-Lingual Word Embeddings for Turkic Languages
Elmurod Kuriyozov | Yerai Doval | Carlos Gómez-Rodríguez
Proceedings of the Twelfth Language Resources and Evaluation Conference

There has been an increasing interest in learning cross-lingual word embeddings to transfer knowledge obtained from a resource-rich language, such as English, to lower-resource languages for which annotated data is scarce, such as Turkish, Russian, and many others. In this paper, we present the first viability study of established techniques to align monolingual embedding spaces for Turkish, Uzbek, Azeri, Kazakh and Kyrgyz, members of the Turkic family which is heavily affected by the low-resource constraint. Those techniques are known to require little explicit supervision, mainly in the form of bilingual dictionaries, hence being easily adaptable to different domains, including low-resource ones. We obtain new bilingual dictionaries and new word embeddings for these languages and show the steps for obtaining cross-lingual word embeddings using state-of-the-art techniques. Then, we evaluate the results using the bilingual dictionary induction task. Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together. Furthermore, evaluation on an extrinsic task (Sentiment analysis on Uzbek) proves that monolingual word embeddings can, although slightly, benefit from cross-lingual alignments.


Improving Cross-Lingual Word Embeddings by Meeting in the Middle
Yerai Doval | Jose Camacho-Collados | Luis Espinosa-Anke | Steven Schockaert
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through linear transformations, using no more than a small bilingual dictionary as supervision. In this work, we propose to apply an additional transformation after the initial alignment step, which moves cross-lingual synonyms towards a middle point between them. By applying this transformation our aim is to obtain a better cross-lingual integration of the vector spaces. In addition, and perhaps surprisingly, the monolingual spaces also improve by this transformation. This is in contrast to the original alignment, which is typically learned such that the structure of the monolingual spaces is preserved. Our experiments confirm that the resulting cross-lingual embeddings outperform state-of-the-art models in both monolingual and cross-lingual evaluation tasks.


LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification
David Vilares | Yerai Doval | Miguel A. Alonso | Carlos Gómez-Rodríguez
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


LYSGROUP: Adapting a Spanish microtext normalization system to English.
Yerai Doval Mosquera | Jesús Vilares | Carlos Gómez-Rodríguez
Proceedings of the Workshop on Noisy User-generated Text


LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English
David Vilares | Miguel Hermo | Miguel A. Alonso | Carlos Gómez-Rodríguez | Yerai Doval
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)