AI explainability : a bridge between machine vision and natural language processing

Oussalah, Mourad (2021-02-21)
 
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https://doi.org/10.1007/978-3-030-68796-0_19

Oussalah, Mourad
Springer Nature
21.02.2021

Oussalah M. (2021) AI Explainability. A Bridge Between Machine Vision and Natural Language Processing. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_19

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© Springer Nature Switzerland AG 2021. This is a post-peer-review, pre-copyedit version of an article published in Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-68796-0_19.
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doi:https://doi.org/10.1007/978-3-030-68796-0_19
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https://urn.fi/URN:NBN:fi-fe2022030422048
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Abstract

This paper attempts to present an appraisal review of explainable Artificial Intelligence research, with a focus on building a bridge between image processing community and natural language processing (NLP) community. The paper highlights the implicit link between the two disciplines as exemplified from the emergence of automatic image annotation systems, visual question-answer systems. Text-To-Image generation and multimedia analytics. Next, the paper identified a set of natural language processing fields where the visual-based explainability can boost the local NLP task. This includes, sentiment analysis, automatic text summarization, system argumentation, topical analysis, among others, which are highly expected to fuel prominent future research in the field.

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