RoMa at HAHA-2021: Deep Reinforcement Learning to Improve a Transformed-based Model for Humor Detection

[EN] In this paper, we describe our system we participated in the shared task ¿Humor Analysis based on Human Annotation (HAHA) at IberLEF-2021 with. Our system relies on data representations learned through fine-tuned neural language models. The representations are used to train a Siamese Neural Network (SNN) which learns to verify whether or not a pair of tweets belong to the same or distinct classes. A key point in our model is the heuristic used to create the pair of messages in the training and test phases. For that, we used a Deep Reinforcement Learning (DRL) strategy that aims at identifying a set of optimal prototypes in each class. In general, the results achieved are encouraging and give us a starting point for further improvements.The work of the second and third authors was in the framework of the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31), funded by Spanish Ministry of Science and Innovation, and DeepPattern (PROMETEO/2019/121), funded by the Generalitat Valenciana.Rodriguez, M.; Ortega-Bueno, R.; Rosso, P. (2021). RoMa at HAHA-2021: Deep Reinforcement Learning to Improve a Transformed-based Model for Humor Detection. CEUR Workshop. 1-8. http://hdl.handle.net/10251/1905551

Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

[EN] Making machines understand language and reasoning on it has been one of the most challenging problems addressed by Artificial Intelligent researchers. This challenge increases when figurative language is used for communicating complex meanings, intentions, emotions and attitudes in creative and funny ways. In fact, sentiment analysis approaches struggle when facing irony, satire and other figurative languages, particularly those where the explanation of a prediction might arguably be as necessary as the prediction itself. This paper describes a new model MvAttLSTM based on deep learning for irony and satire detection in tweets written in distinct Spanish variants. The proposed model is based on an attentive-LSTM informed with three additional views learned from distinct perspectives. We investigate two strategies to pass these views into MvAttLSTM. We perform an extensive evaluation on three corpora, one for irony detection and two for satire detection. Moreover, in order to study the robustness of our proposed model, we investigate its performance on humor recognition. Experiments confirm that the proposed views help our model to improve its performance. Moreover, they show that affective information benefits our model to detect irony and satire. In particular, a first analysis of the results highlights the discriminating power of emotional features obtained from SenticNet and SEL lexicon. Overall, our system achieves the state-of-the-art performance in irony and satire detection in Spanish variants and competitive results in humor recognition.The work of the first two authors was in the framework of the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31) , funded by Spanish Ministry of Science and Innovation, and DeepPattern (PROMETEO/2019/121) , funded by the Generalitat Valenciana, Spain.Ortega-Bueno, R.; Rosso, P.; Medina-Pagola, JE. (2022). Multi-view informed attention-based model for Irony and Satire detection in Spanish variants. Knowledge-Based Systems. 235:1-24. https://doi.org/10.1016/j.knosys.2021.10759712423

Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification Task

[EN] In this paper, we describe the system proposed by UO-UPV team for addressing the task Profiling Hate Speech Spreaders on Twitter shared at PAN 2021. The system relies on a modular architecture, combining Deep Learning models with an introduced variant of the Impostor Method (IM). It receives a single profile composed of a fixed quantity of tweets. These posts are encoded as dense feature vectors using a fine-tuned transformer model and later combined to represent the whole profile. For classifying a new profile as hate speech spreader or not, it is compared by a similarity function with the Impostor Method with respect to random sampled prototypical profiles. In the final evaluation phase, our model achieved 74% and 82% of accuracy for English and Spanish languages respectively, ranking our team at 2¿¿ position and giving a starting point for further improvements.The work of the third author was in the framework of the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31), funded by Spanish Ministry of Science and Innovation, and DeepPattern (PROMETEO/2019/121), funded by the Generalitat Valenciana.Labadie Tamayo, R.; Castro Castro, D.; Ortega-Bueno, R. (2021). Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification Task. CEUR. 2035-2046. http://hdl.handle.net/10251/1906692035204

Transformer-based models for multimodal irony detection

Irony is nowadays a pervasive phenomenon in social networks. The multimodal functionalities of these platforms (i.e., the possibility to attach audio, video, and images to textual information) are increasingly leading their users to employ combinations of information in different formats to express their ironic thoughts. The present work focuses on the study of irony detection in social media posts involving image and text. To this end, a transformer architecture for the fusion of textual and image information is proposed. The model leverages disentangled text attention with visual transformers, improving F1-score up to 9% over previous existing works in the field and current state-of-the-art visio-linguistic transformers. The proposed architecture was evaluated in three different multimodal datasets gathered from Twitter and Tumblr. The results revealed that, in many situations, the text-only version of the architecture was able to capture the ironic nature of the message without using visual information. This phenomenon was further analysed, leading to the identification of linguistic patterns that could provide the context necessary for irony detection without the need for additional visual information.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministry of Science and Innovation and Fondo Europeo de Desarrollo Regional (FEDER) in the framework of project “Technological Resources for Intelligent VIral AnaLysis through NLP (TRIVIAL)” (PID2021-122263OB-C22)

UOBIT @ TAG-it: Exploring a Multi-faceted Representation for Profiling Age, Topic and Gender in Italian Texts

This paper describes our system for participating in the TAG-it Author Profiling task at EVALITA 2020. The task aims to predict age and gender of blogs users from their posts, as the topic they wrote about. Our proposal combines learned representations by RNN at word and sentence levels, Transformer Neural Nets and hand-crafted stylistic features. All these representations are mixed and fed into a fully connected layer from a feed-forward neural network in order to make predictions for addressed subtasks. Experimental results show that our model achieves encouraging performance

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Predicción de la evolución de comunidades en redes sociales

Muchos investigadores se han volcado en la tarea de analizar y modelar el comportamiento de las redes sociales, debido al auge que han tomado. Son varias las tareas llevadas a cabo como parte de su analisis. Dentro de ellas destacan por su importancia, la descripcion y prediccion de la evolucion de las comunidades que conforman la red. Esta ultima es tratada desde la perspectiva de las distintas formas que tienen las comunidades en la red; analizando su comportamiento en la evolucion. En este trabajo se propone un metodo para la prediccion de la evolucion de comunidades en redes sociales basado en subgrafos frecuentes. Finalmente nuestra propuesta es comparada con un enfoque recientemente reportado en la literatura, obteniendo resultados similares