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The Natural Language Pipeline, Neural Text Generation and Explainability

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 18 Dec 2020 English Publisher:ZenodoFunded by:EC | NL4XAI

Authors: Faille, Juliette; Gatt, Albert; Gardent, Claire;

doi: 10.5281/zenodo.5887675 , 10.5281/zenodo.5887676

The Natural Language Pipeline, Neural Text Generation and Explainability

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Abstract

End-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into sub-modules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for breaking up the end-to-end encoder-decoder. We survey recent papers that integrate traditional NLG submodules in neural approaches and analyse their explainability. Our survey is a first step towards building explainable neural NLG models.

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