Sequence-to-Sequence Spanish Pre-trained Language Models

In recent years, substantial advancements in pre-trained language models have paved the way for the development of numerous non-English language versions, with a particular focus on encoder-only and decoder-only architectures. While Spanish language models encompassing BERT, RoBERTa, and GPT have exhibited prowess in natural language understanding and generation, there remains a scarcity of encoder-decoder models designed for sequence-to-sequence tasks involving input-output pairs. This paper breaks new ground by introducing the implementation and evaluation of renowned encoder-decoder architectures, exclusively pre-trained on Spanish corpora. Specifically, we present Spanish versions of BART, T5, and BERT2BERT-style models and subject them to a comprehensive assessment across a diverse range of sequence-to-sequence tasks, spanning summarization, rephrasing, and generative question answering. Our findings underscore the competitive performance of all models, with BART and T5 emerging as top performers across all evaluated tasks. As an additional contribution, we have made all models publicly available to the research community, fostering future exploration and development in Spanish language processing

Layered Composites Based on Recycled PET/Functionalized Woven Flax Fibres

Extended Abstract Plastic waste is generated by a variety of sources including packaging, automotive, consumer goods, electrical and electronics industries, leading to a significant growth in the volume of waste and the impetuous need to reduce it The paper aims at developing new layered composite materials based on recycled thermoplastic polymer (PETpolyethylene terephthalate) from the food industry reinforced with woven flax fiber functionalized with nano (micro) particles of titanium or alumina and testing the composite in terms of physico-mechanical (tensile strength, bending, shock, etc.), morphological (SEM), structural (FTIR), and thermal (Vicat) properties. Based on this technology, the new composite will exhibit improved physical, mechanical and thermal properties, as well as resistance to mold attack. In this regard, in the first stage, the surface of flax fibers were chemically modified using aluminum (AlCl3), and titanium (titanium butoxide) precursors followed by precipitation. The woven flax whose surface was functionalized with nano (micro) alumina or TiO2 particles were subsequently used to obtain layered composite materials. Layered composite materials were obtained by alternating functionalized / not functionalized woven flax fiber with sheets made from recycled PET. The recycled PET sheets and layered composites based on recycled PET and functionalized / not functionalized woven flax fiber were obtained by press molding using an electrical press at the following optimum parameters: plate temperature -254ºC, preheating time -8 min; pressing time -2 min; cooling time -15 min; pressing force -100 kN. Special attention must be paid to the pre-drying process (at 100-110ºC) to remove adsorbed water. In the absence of the pre-drying operation, the resulting sheets exhibit holes, porosity and discontinuities, making them unusable for the development of layered composite materials. Physical, mechanical and thermal analyses results for specimens of layered composite materials based on recycled PET / functionalised woven flax fiber show significantly improved values compared with the control samples obtained from recycled PET / not functionalized flax fiber. Improved mechanical and thermal properties are due to links developed at the woven flax fiber / polymer phase interphase. Results have also been confirmed by SEM, while the degree of adhesion and the interpenetration of polymer phase / woven flax fiber are superior in the case of composites made of functionalized flax fibers in comparison with the unfunctionalized ones

Diagnostic Classifiers for Explaining a Neural Model with Hierarchical Attention for Aspect-based Sentiment Classification

The current models proposed for aspect-based sentiment classification (ABSC) are mainly developed with the purpose of providing high rates of accuracy, regardless of the inner working which is usually difficult to understand. Considering the state-of-art model LCR-Rot-hop++ for ABSC, we use diagnostic classifiers to gain insights into the encoded information of each layer. Starting from a set of various hypotheses, we test how sentimentrelated information is captured by different layers of the model. Given the model architecture, information about the related words to the target is easily extracted. Also, the model is able to detect to some extent information about the sentiments of the words and, in particular, sentiments of the words related to the target. However, the model is less effective in extracting the aspect mentions associated with a word and the general structure of the sentence.</p

Diagnostic Classifiers for Explaining a Neural Model with Hierarchical Attention for Aspect-based Sentiment Classification

The current models proposed for aspect-based sentiment classification (ABSC) are mainly developed with the purpose of providing high rates of accuracy, regardless of the inner working which is usually difficult to understand. Considering the state-of-art model LCR-Rot-hop++ for ABSC, we use diagnostic classifiers to gain insights into the encoded information of each layer. Starting from a set of various hypotheses, we test how sentimentrelated information is captured by different layers of the model. Given the model architecture, information about the related words to the target is easily extracted. Also, the model is able to detect to some extent information about the sentiments of the words and, in particular, sentiments of the words related to the target. However, the model is less effective in extracting the aspect mentions associated with a word and the general structure of the sentence.</p

Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification

Many high performance machine learning models for Aspect-Based Sentiment Classification (ABSC) produce black box models, and therefore barely explain how they classify a certain sentiment value towards an aspect. In this paper, we propose explanation models, that inspect the internal dynamics of a state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique called Diagnostic Classification. Our diagnostic classifier is a simple neural network, which evaluates whether the internal layers of the LCR-Rot-hop model encode useful word information for classification, i.e., the part of speech, the sentiment value, the presence of aspect relation, and the aspect-related sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop model encode the part of speech and the sentiment value, whereas the higher layers represent the presence of a relation with the aspect and the aspect-related sentiment value of words.Comment: The 36th ACM/SIGAPP Symposium On Applied Computing, Virtual Conference, March 22-March 26, 202

Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysis

Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. This strategy has been widely used in image classification but has to the best of our knowledge not yet been used in aspect-based sentiment analysis (ABSA). ABSA is a text analysis technique that determines aspects and their associated sentiment in opinionated text. In this paper, we investigate the effect of data augmentation on a state-of-the-art hybrid approach for aspect-based sentiment analysis (HAABSA). We apply modified versions of easy data augmentation (EDA), backtranslation, and word mixup. We evaluate the proposed techniques on the SemEval 2015 and SemEval 2016 datasets. The best result is obtained with the adjusted version of EDA, which yields a 0.5 percentage point improvement on the SemEval 2016 dataset and 1 percentage point increase on the SemEval 2015 dataset compared to the original HAABSA model.Comment: The 36th ACM/SIGAPP Symposium On Applied Computing, Virtual Conference, March 22-March 26, 20

Antimicrobial Packaging for Plum Tomatoes Based on ZnO Modified Low-Density Polyethylene

Food safety and quality are major concerns in the food industry. Despite numerous studies, polyethylene remains one of the most used materials for packaging due to industry reluctance to invest in new technologies and equipment. Therefore, modifications to the current materials are easier to implement than adopting whole new solutions. Antibacterial activity can be induced in low-density polyethylene films only by adding antimicrobial agents. ZnO nanoparticles are well known for their strong antimicrobial activity, coupled with low toxicity and UV shielding capability. These characteristics recommend ZnO for the food industry. By incorporating such safe and dependable antimicrobial agents in the polyethylene matrix, we have obtained composite films able to inhibit microorganisms’ growth that can be used as packaging materials. Here we report the obtaining of highly homogenous composite films with up to 5% ZnO by a melt mixing process at 150 °C for 10 min. The composite films present good transparency in the visible domain, permitting consumers to visualize the food, but have good UV barrier properties. The composite films exhibit good antimicrobial and antibiofilm activity from the lowest ZnO composition (1%), against both Gram-positive and Gram-negative bacterial strains. The homogenous dispersion of ZnO nanoparticles into the polyethylene matrix was assessed by Fourier transform infrared microscopy and scanning electron microscopy. The optimal mechanical barrier properties were obtained for composition with 3% ZnO. The thermal analysis indicates that the addition of ZnO nanoparticles has increased thermal stability by more than 100 °C. The UV-Vis spectra indicate a low transmittance in the UV domain, lower than 5%, making the films suitable for blocking photo-oxidation processes. The obtained films proved to be efficient packaging films, successfully preserving plum (Rome) tomatoes for up to 14 days

Poly(3-hydroxybutyrate) Modified by Nanocellulose and Plasma Treatment for Packaging Applications

In this work, a new eco-friendly method for the treatment of poly(3-hydroxybutyrate) (PHB) as a candidate for food packaging applications is proposed. Poly(3-hydroxybutyrate) was modified by bacterial cellulose nanofibers (BC) using a melt compounding technique and by plasma treatment or zinc oxide (ZnO) nanoparticle plasma coating for better properties and antibacterial activity. Plasma treatment preserved the thermal stability, crystallinity and melting behavior of PHB‒BC nanocomposites, regardless of the amount of BC nanofibers. However, a remarkable increase of stiffness and strength and an increase of the antibacterial activity were noted. After the plasma treatment, the storage modulus of PHB having 2 wt % BC increases by 19% at room temperature and by 43% at 100 &#176;C. The tensile strength increases as well by 21%. In addition, plasma treatment also inhibits the growth of Staphylococcus aureus and Escherichia coli by 44% and 63%, respectively. The ZnO plasma coating led to important changes in the thermal and mechanical behavior of PHB‒BC nanocomposite as well as in the surface structure and morphology. Strong chemical bonding of the metal nanoparticles on PHB surface following ZnO plasma coating was highlighted by infrared spectroscopy. Moreover, the presence of a continuous layer of self-aggregated ZnO nanoparticles was demonstrated by scanning electron microscopy, ZnO plasma treatment completely inhibiting growth of Staphylococcus aureus. A plasma-treated PHB‒BC nanocomposite is proposed as a green solution for the food packaging industry

Control of Dielectric and Mechanical Properties of Styrenic Block Copolymer by Graphite Incorporation

The structure&ndash;property relationship of dielectric elastomers, as well as the methods of improving the control of this relationship, has been widely studied over the last few years, including in some of our previous works. In this paper, we study the control, improvement, and correlation, for a significant range of temperatures, of the mechanical and dielectric properties of polystyrene-b-(ethylene-co-butylene)-b-styrene (SEBS) and maleic-anhydride-grafted SEBS (SEBS-MA) by using graphite (G) as filler in various concentrations. The aim is to analyze the suitability of these composites for converting electrical energy into mechanical energy or vice versa. The dielectric spectroscopy analysis performed in the frequency range of 10 to 1 MHz and at temperatures between 27 and 77 &deg;C emphasized an exponential increase in real permittivity with G concentration, a low level of dielectric losses (&asymp;10&minus;3), as well as the stability of dielectric losses with temperature for high G content. These results correlate well with the increase in mechanical stiffness with an increase in G content for both SEBS/G and SEBS-MA/G composites. The activation energies for the dielectric relaxation processes detected in SEBS/G and SEBS-MA/G composites were also determined and discussed in connection with the mechanical, thermal, and structural properties resulting from thermogravimetric analysis, differential scanning calorimetry, Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy analyses