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Image Schemas and Image Schematic Complexes: Enhancing Neural Machine Translation Networks

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Computational and Corpus-Based Phraseology (EUROPHRAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13528))

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Abstract

Machine Translation (MT) is a Natural Language Processing (NLP) application which has taken off and reported considerable progress in recent years. Most recent applications of MT employ neural networks imitating the principles of human understanding and creation of meaning at conceptual and cognitive levels (Nerlich and Clarke 2000: 141). They are based on techniques which try to simulate the mechanisms of learning in biological organisms carried out through the neurons (Aggarwal 2018: 1; Theordoris 2020: 903). However, human intelligence and the cognitive models which humans use through language should be subject to more examination in order to have the capacity to unveil more basic cognitive features. For this reason, a more holistic understanding of certain aspects of human intelligence based on cognitive models is still required in order to take machine learning a step further (Goertzel et al. 2012: 124). Image schemas and image schematic complexes are among the cognitive issues which would benefit from further studies as its basic structure is fundamental in natural language processing and conceptualisation (Hedblom et al. 2019). They are common in all languages and all cultures but their use is not always universal. This variation influences the quality of MT, as in some cases, this variance is not taken into consideration while feeding the neural networks of MT. This preliminary study has the objective of studying the novel idea of image schemas and image schematic complexes and proposing an applied methodology to use them in MT.

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Acknowledgments

This research was carried out as part of the project PID2020-118369GB-I00, Transversal integration of culture into an environmental terminological knowledge base (TRANSCULTURE), funded by the Spanish Ministry of Science and Innovation. Funding was also provided by an FPU grant (FPU18/05327) given by the Spanish Ministry of Education.

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  1. Universidad de Granada, 18071, Granada, Spain

    Amal Haddad Haddad

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  1. Amal Haddad Haddad
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Correspondence to Amal Haddad Haddad .

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  1. University of Malaga, Malaga, Spain

    Gloria Corpas Pastor

  2. University of Wolverhampton, Wolverhampton, UK

    Ruslan Mitkov

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Haddad Haddad, A. (2022). Image Schemas and Image Schematic Complexes: Enhancing Neural Machine Translation Networks. In: Corpas Pastor, G., Mitkov, R. (eds) Computational and Corpus-Based Phraseology. EUROPHRAS 2022. Lecture Notes in Computer Science(), vol 13528. Springer, Cham. https://doi.org/10.1007/978-3-031-15925-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-15925-1_8

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