A binarized neural network joint model for machine translation

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Abstract

The neural network joint model (NNJM), which augments the neural network language model (NNLM) with an m-word source context window, has achieved large gains in machine translation accuracy, but also has problems with high normalization cost when using large vocabularies. Training the NNJM with noise-contrastive estimation (NCE), instead of standard maximum likelihood estimation (MLE), can reduce computation cost. In this paper, we propose an alternative to NCE, the binarized NNJM (BNNJM), which learns a binary classifier that takes both the context and target words as input, and can be efficiently trained using MLE. We compare the BNNJM and NNJM trained by NCE on various translation tasks.

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Zhang, J., Utiyama, M., Sumita, E., Neubig, G., & Nakamura, S. (2015). A binarized neural network joint model for machine translation. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 2094–2099). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1250

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