Marc Franco-Salvador


Few-Shot Learning with Siamese Networks and Label Tuning
Thomas Müller | Guillermo Pérez-Torró | Marc Franco-Salvador
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks. In this work, we show that with proper pre-training, Siamese Networks that embed texts and labels offer a competitive alternative. These models allow for a large reduction in inference cost: constant in the number of labels rather than linear. Furthermore, we introduce label tuning, a simple and computationally efficient approach that allows to adapt the models in a few-shot setup by only changing the label embeddings. While giving lower performance than model fine-tuning, this approach has the architectural advantage that a single encoder can be shared by many different tasks.


What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts
Sanja Stajner | Seren Yenikent | Bilal Ghanem | Marc Franco-Salvador
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people’s everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.

Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification
Angelo Basile | Guillermo Pérez-Torró | Marc Franco-Salvador
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Emotion Classification is the task of automatically associating a text with a human emotion. State-of-the-art models are usually learned using annotated corpora or rely on hand-crafted affective lexicons. We present an emotion classification model that does not require a large annotated corpus to be competitive. We experiment with pretrained language models in both a zero-shot and few-shot configuration. We build several of such models and consider them as biased, noisy annotators, whose individual performance is poor. We aggregate the predictions of these models using a Bayesian method originally developed for modelling crowdsourced annotations. Next, we show that the resulting system performs better than the strongest individual model. Finally, we show that when trained on few labelled data, our systems outperform fully-supervised models.


Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning
Mara Chinea-Rios | Marc Franco-Salvador | Yassine Benajiba
Proceedings of the Twelfth Language Resources and Evaluation Conference

The task of aspect extraction is an important component of aspect-based sentiment analysis. However, it usually requires an expensive human post-processing to ensure quality. In this work we introduce Aspect On, an interactive solution based on online learning that allows users to post-edit the aspect extraction with little effort. The Aspect On interface shows the aspects extracted by a neural model and, given a dataset, annotates its words with the corresponding aspects. Thanks to the online learning, Aspect On updates the model automatically and continuously improves the quality of the aspects displayed to the user. Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.


SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
Angelo Basile | Marc Franco-Salvador | Neha Pawar | Sanja Štajner | Mara Chinea Rios | Yassine Benajiba
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.


CATS: A Tool for Customized Alignment of Text Simplification Corpora
Sanja Štajner | Marc Franco-Salvador | Paolo Rosso | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Single and Cross-domain Polarity Classification using String Kernels
Rosa M. Giménez-Pérez | Marc Franco-Salvador | Paolo Rosso
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

The polarity classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain polarity classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain polarity classification.

Sentence Alignment Methods for Improving Text Simplification Systems
Sanja Štajner | Marc Franco-Salvador | Simone Paolo Ponzetto | Paolo Rosso | Heiner Stuckenschmidt
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.


UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
Marc Franco-Salvador | Sudipta Kar | Thamar Solorio | Paolo Rosso
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


Distributed Representations of Words and Documents for Discriminating Similar Languages
Marc Franco-Salvador | Paolo Rosso | Francisco Rangel
Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects


A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization
Marc Franco-Salvador | Paolo Rosso | Roberto Navigli
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics