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A light method for data generation: a combination of Markov Chains and Word Embeddings.

Identificadores del recurso

1135-5948

http://hdl.handle.net/10641/2327

10.26342/2020-64-10

Jatorria

(Repositorio Institucional de la Universidad Francisco de Vitoria)

Fitxa

Izenburu:: A light method for data generation: a combination of Markov Chains and Word Embeddings.; Un método ligero de generación de datos: combinación entre Cadenas de Markov y Word Embeddings.
Tema:: Generation; Hybrid; Markov Chains; Embeddings; Similarity
Deskribapen:: Most of the current state-of-the-art Natural Language Processing (NLP) techniques are highly data-dependent. A significant amount of data is required for their training, and in some scenarios data is scarce. We present a hybrid method to generate new sentences for augmenting the training data. Our approach takes advantage of the combination of Markov Chains and word embeddings to produce high-quality data similar to an initial dataset. In contrast to other neural-based generative methods, it does not need a high amount of training data. Results show how our approach can generate useful data for NLP tools. In particular, we validate our approach by building Transformer-based Language Models using data from three different domains in the context of enriching general purpose chatbots.; post-print; 1,74 MB
Idioma:: English
Harreman:: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6199
Autor/Productor:: Martínez García, Eva; Nogales Moyano, Alberto; Morales Escudero, Javier; García Tejedor, Álvaro José
Argitaratzaile:: Procesamiento del Lenguaje Natural
Eskubideak:: Atribución-NoComercial-SinDerivadas 3.0 España; http://creativecommons.org/licenses/by-nc-nd/3.0/es/; openAccess
Data:: 2021-06-16T08:19:29Z; 2020
Tipo de recurso:: article

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        <field name="value">Martínez García, Eva</field>
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        <field name="value">Nogales Moyano, Alberto</field>
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        <field name="value">Morales Escudero, Javier</field>
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        <field name="value">García Tejedor, Álvaro José</field>
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        <field name="value">Most of the current state-of-the-art Natural Language Processing (NLP) techniques are highly data-dependent. A significant amount of data is required for their training, and in some scenarios data is scarce. We present a hybrid method to generate new sentences for augmenting the training data. Our approach takes advantage of the combination of Markov Chains and word embeddings to produce high-quality data similar to an initial dataset. In contrast to other neural-based generative methods, it does not need a high amount of training data. Results show how our approach can generate useful data for NLP tools. In particular, we validate our approach by building Transformer-based Language Models using data from three different domains in the context of enriching general purpose chatbots.</field>
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        <field name="value">Un método ligero de generación de datos: combinación entre Cadenas de Markov y Word Embeddings.</field>
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