scholarly journals Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation

2021 ◽  
Author(s):  
Alexandra Chronopoulou ◽  
Dario Stojanovski ◽  
Alexander Fraser
Keyword(s):  
Machine Translation ◽  
Language Models ◽  
Neural Machine Translation

scholarly journals Unsupervised Neural Machine Translation with SMT as Posterior Regularization

2019 ◽  
Vol 33 ◽  
pp. 241-248 ◽  
Author(s):  
Shuo Ren ◽  
Zhirui Zhang ◽  
Shujie Liu ◽  
Ming Zhou ◽  
Shuai Ma
Keyword(s):  
Machine Translation ◽  
Language Models ◽  
Translation Process ◽  
Weak Supervision ◽  
Neural Machine Translation ◽  
Back Translation ◽  
Negative Effect ◽  
Model Training ◽  
Cross Lingual ◽  
Pseudo Data

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.

scholarly journals Towards Making the Most of BERT in Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 9378-9385
Author(s):  
Jiacheng Yang ◽  
Mingxuan Wang ◽  
Hao Zhou ◽  
Chengqi Zhao ◽  
Weinan Zhang ◽  
...  
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Fine Tuning ◽  
Language Models ◽  
German Language ◽  
Neural Machine Translation ◽  
Dynamic Switching ◽  
Previous State ◽  
Language Pair

GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various natural language processing tasks. However, LM fine-tuning often suffers from catastrophic forgetting when applied to resource-rich tasks. In this work, we introduce a concerted training framework (CTnmt) that is the key to integrate the pre-trained LMs to neural machine translation (NMT). Our proposed CTnmt} consists of three techniques: a) asymptotic distillation to ensure that the NMT model can retain the previous pre-trained knowledge; b) a dynamic switching gate to avoid catastrophic forgetting of pre-trained knowledge; and c) a strategy to adjust the learning paces according to a scheduled policy. Our experiments in machine translation show CTnmt gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1.4 BLEU score. While for the large WMT14 English-French task with 40 millions of sentence-pairs, our base model still significantly improves upon the state-of-the-art Transformer big model by more than 1 BLEU score.

scholarly journals An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

2019 ◽  
Vol 28 (4) ◽  
pp. 1-29 ◽  
Author(s):  
Michele Tufano ◽  
Cody Watson ◽  
Gabriele Bavota ◽  
Massimiliano Di Penta ◽  
Martin White ◽  
...  
Keyword(s):  
Empirical Study ◽  
Machine Translation ◽  
Neural Machine Translation ◽  
Bug Fixing ◽  
In The Wild

scholarly journals Neural Machine Translation for Semantic-Driven Q&A Systems in the Factory Planning

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 9-14
Author(s):  
Uwe Dombrowski ◽  
Alexander Reiswich ◽  
Raphael Lamprecht
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Factory Planning

Neural machine translation with a polysynthetic low resource language

2020 ◽  
Vol 34 (4) ◽  
pp. 325-346
Author(s):  
John E. Ortega ◽  
Richard Castro Mamani ◽  
Kyunghyun Cho
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Low Resource
Sign in / Sign up

Export Citation Format

Share Document