Journal of Natural Language Processing
Online ISSN : 2185-8314
Print ISSN : 1340-7619
ISSN-L : 1340-7619
General Paper (Peer-Reviewed)
Neural Machine Translation Based on Insertion of Target-Side NE Tags
Naoki MinamibataAkihiro TamuraTsuneo Kato
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2024 Volume 31 Issue 2 Pages 610-636

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Abstract

In the field of neural machine translation (NMT), translation performance has been improved using named entity (NE) information. In earlier studies, two promising approaches for NE-based NMT have been proposed: a “tagging model” that inserts NE tags into sentences and an “embedding model” that incorporates NE embeddings into word embeddings. Although an embedding model improves translation performance by using target-side NE information in addition to source-side NE information, tagging models use only source-side NE information. Therefore, this study proposes a new tagging model that uses both source- and target-side NE information. Moreover, this study proposes an ensemble of our tagging model and an embedding model that generates a target language sentence based on the probabilities of averaging the output probabilities of the tagging model and those of the embedding model. Evaluations on the WMT 2014 English (En)↔German (De) translation tasks and the WMT 2020 English (En)↔Japanese (Ja) translation tasks showed that the proposed tagging model outperformed an existing tagging model (up to +0.76, +1.59, +0.96, and +0.65 BLEU for En-to-De, De-to-En, En-to-Ja, and Ja-to-En, respectively).

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© 2024 The Association for Natural Language Processing
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