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NUBIA: NeUral Based Interchangeability Assessor for Text Generation

2020-04-30 10:11:33
Hassan Kane, Muhammed Yusuf Kocyigit, Ali Abdalla, Pelkins Ajanoh, Mohamed Coulibali
arXiv_CL
           
Abstract

We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA which outperforms metrics currently used to evaluate machine translation, summaries and slightly exceeds/matches state of the art metrics on correlation with human judgement on the WMT segment-level Direct Assessment task, sentence-level ranking and image captioning evaluation. The model implemented is modular, explainable and set to continuously improve over time.

Abstract (translated)

URL

https://arxiv.org/abs/2004.14667

PDF

https://arxiv.org/pdf/2004.14667.pdf

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