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The Ethos of Artificial Intelligence as a Legal Personality in a Globalized Space: Examining the Overhaul of the Post-liberal Technological Order Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation

Authors : Despoina Mouratidis, Katia Lida Kermanidis, Vilelmini Sosoni

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

A language independent deep learning (DL) architecture for machine translation (MT) evaluation is presented. This DL architecture aims at the best choice between two MT (S1, S2) outputs, based on the reference translation (Sr) and the annotation score. The outputs were generated from a statistical machine translation (SMT) system and a neural machine translation (NMT) system. The model applied in two language pairs: English - Greek (EN-EL) and English - Italian (EN-IT). In this paper, a variety of experiments with different parameter configurations is presented. Moreover, linguistic features, embeddings representation and natural language processing (NLP) metrics (BLEU, METEOR, TER, WER) were tested. The best score was achieved when the proposed model used source segments (SSE) information and the NLP metrics set. Classification accuracy has increased up to 5% (compared to previous related work) and reached quite satisfactory results for the Kendall τ score.

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Metadata
Title
Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation
Authors
Despoina Mouratidis
Katia Lida Kermanidis
Vilelmini Sosoni
Copyright Year
2020
Book
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-49185-7

Electronic ISBN: 978-3-030-49186-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-49186-4_7

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