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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

February 1, 2023

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Authors

Rumeng Li

Umass Amherst


Xun Wang

Umass Lowell


Hong Yu

Umass Lowell


DOI:

10.1609/aaai.v34i05.6339


Abstract:

Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational space to generalize the model to different domains. We mimic the domain adaptation of the machine translation model to low-resource domains using multiple translation tasks on different domains. A new training strategy based on meta-learning is developed along with the proposed model to update the model parameters and meta parameters alternately. Experiments on datasets of different domains showed substantial improvements of NMT performances on a limited amount of data.

Topics: AAAI

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HOW TO CITE:

Rumeng Li||Xun Wang||Hong Yu MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 8245-8252.

Rumeng Li||Xun Wang||Hong Yu MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation AAAI 2020, 8245-8252.

Rumeng Li||Xun Wang||Hong Yu (2020). MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 8245-8252.

Rumeng Li||Xun Wang||Hong Yu. MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.8245-8252.

Rumeng Li||Xun Wang||Hong Yu. 2020. MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation. "Proceedings of the AAAI Conference on Artificial Intelligence". 8245-8252.

Rumeng Li||Xun Wang||Hong Yu. (2020) "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation", Proceedings of the AAAI Conference on Artificial Intelligence, p.8245-8252

Rumeng Li||Xun Wang||Hong Yu, "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation", AAAI, p.8245-8252, 2020.

Rumeng Li||Xun Wang||Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.8245-8252.

Rumeng Li||Xun Wang||Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 8245-8252.

Rumeng Li||Xun Wang||Hong Yu. MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation. AAAI[Internet]. 2020[cited 2023]; 8245-8252.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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