Better Neural Machine Translation by Extracting Linguistic Information from BERT

• URL: http://arxiv.org/abs/2104.02831v1
• Date: Wed, 7 Apr 2021 00:03:51 GMT
• Title: Better Neural Machine Translation by Extracting Linguistic Information from BERT
• Authors: Hassan S. Shavarani and Anoop Sarkar
• Abstract summary: Adding linguistic information to neural machine translation (NMT) has mostly focused on using point estimates from pre-trained models. We augment NMT by extracting dense fine-tuned vector-based linguistic information from BERT instead of using point estimates.
• Score: 4.353029347463806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adding linguistic information (syntax or semantics) to neural machine translation (NMT) has mostly focused on using point estimates from pre-trained models. Directly using the capacity of massive pre-trained contextual word embedding models such as BERT (Devlin et al., 2019) has been marginally useful in NMT because effective fine-tuning is difficult to obtain for NMT without making training brittle and unreliable. We augment NMT by extracting dense fine-tuned vector-based linguistic information from BERT instead of using point estimates. Experimental results show that our method of incorporating linguistic information helps NMT to generalize better in a variety of training contexts and is no more difficult to train than conventional Transformer-based NMT.

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