Work

An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation

Year: 2017

Type: conference-paper

Abstract: In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT).We combine two existing approaches namely fine tuning and multi domain NMT.We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-ofdomain corpora.All corpora are augmented with artificial tags to indicate specific domains.We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p17-2061

Authors: Chenhui Chu, Raj Dabre, Sadao Kurohashi

Institutions: The University of Osaka, Kyoto University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Department of Science and Technology, Ministry of Science and Technology, India, Japan Society for the Promotion of Science

OpenAlex ID: W2744813330