Modelos de aprendizaje profundo para comprensión de textos y una implementación prototípica de GPT-2 para una tarea específica de generación de lenguaje natural
Since 2013, the connectionist paradigm in Natural Language Processing (NLP) has resurged in academic circles by means of new architectures to be adopted later by the software industry with the use of great computing power. It is a truly algorithmic revolution, known as Deep Learning. Several models...
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| Autores principales: | , , , , |
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| Formato: | Online |
| Lenguaje: | spa |
| Publicado: |
Instituto de Lingüística, Facultad de Filosofía y Letras, Universidad Nacional de Cuyo
2021
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| Acceso en línea: | https://revistas.uncu.edu.ar/ojs3/index.php/analeslinguistica/article/view/5524 |
| Sumario: | Since 2013, the connectionist paradigm in Natural Language Processing (NLP) has resurged in academic circles by means of new architectures to be adopted later by the software industry with the use of great computing power. It is a truly algorithmic revolution, known as Deep Learning. Several models have been offered in a speedy race in order to improve state-of-the-art metrics for general domain NLP tasks, according to the most frequentlly used standards (BLEU, GLUE, SuperGLUE). From 2018 onwards, Deep Learning models have attracted even more attention through the so-called Transformers revolution (ELMo, BERT y GPT-2). In this paper, we propose a brief yet exhaustive survey on the models that have been evolving during this last decade. We also describe in detail a complete from scratch implementation for the most recent open-source model GPT-2, fine-tuned for a specific NLG task of slogan generation for commercial products. |
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