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.
@article{arxiv.2104.02831,
title = {Better Neural Machine Translation by Extracting Linguistic Information from BERT},
author = {Hassan S. Shavarani and Anoop Sarkar},
journal= {arXiv preprint arXiv:2104.02831},
year = {2021}
}