scholarly journals Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation

2019 ◽  
Author(s):  
Xin Tan ◽  
Longyin Zhang ◽  
Deyi Xiong ◽  
Guodong Zhou
Keyword(s):  
Machine Translation ◽  
Hierarchical Modeling ◽  
Neural Machine Translation ◽  
Global Context ◽  
Document Level

Enhancing Lexical Translation Consistency for Document-Level Neural Machine Translation

2022 ◽  
Vol 21 (3) ◽  
pp. 1-21
Author(s):  
Xiaomian Kang ◽  
Yang Zhao ◽  
Jiajun Zhang ◽  
Chengqing Zong
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
Test Set ◽  
Neural Machine Translation ◽  
Global Context ◽  
Translation Quality ◽  
Sentence Level ◽  
Document Level

Document-level neural machine translation (DocNMT) has yielded attractive improvements. In this article, we systematically analyze the discourse phenomena in Chinese-to-English translation, and focus on the most obvious ones, namely lexical translation consistency. To alleviate the lexical inconsistency, we propose an effective approach that is aware of the words which need to be translated consistently and constrains the model to produce more consistent translations. Specifically, we first introduce a global context extractor to extract the document context and consistency context, respectively. Then, the two types of global context are integrated into a encoder enhancer and a decoder enhancer to improve the lexical translation consistency. We create a test set to evaluate the lexical consistency automatically. Experiments demonstrate that our approach can significantly alleviate the lexical translation inconsistency. In addition, our approach can also substantially improve the translation quality compared to sentence-level Transformer.

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

2020 ◽  
Author(s):  
Zaixiang Zheng ◽  
Xiang Yue ◽  
Shujian Huang ◽  
Jiajun Chen ◽  
Alexandra Birch
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Local Context ◽  
Unified Approach ◽  
Neural Machine Translation ◽  
Global Context ◽  
Level Data ◽  
Sentence Level ◽  
Target Languages ◽  
Document Level

Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted. We argue that previous research did not make a clear use of the global context, and propose a new document-level NMT framework that deliberately models the local context of each sentence with the awareness of the global context of the document in both source and target languages. We specifically design the model to be able to deal with documents containing any number of sentences, including single sentences. This unified approach allows our model to be trained elegantly on standard datasets without needing to train on sentence and document level data separately. Experimental results demonstrate that our model outperforms Transformer baselines and previous document-level NMT models with substantial margins of up to 2.1 BLEU on state-of-the-art baselines. We also provide analyses which show the benefit of context far beyond the neighboring two or three sentences, which previous studies have typically incorporated.

scholarly journals Document-Level Adaptation for Neural Machine Translation

2018 ◽  
Author(s):  
Sachith Sri Ram Kothur ◽  
Rebecca Knowles ◽  
Philipp Koehn
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Document Level

scholarly journals Document-Level Neural Machine Translation with Hierarchical Attention Networks

2018 ◽  
Author(s):  
Lesly Miculicich ◽  
Dhananjay Ram ◽  
Nikolaos Pappas ◽  
James Henderson
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Attention Networks ◽  
Document Level

scholarly journals Dual contextual module for neural machine translation

2021 ◽  
Author(s):  
Isaac Kojo Essel Ampomah ◽  
Sally McClean ◽  
Glenn Hawe
Keyword(s):  
Machine Translation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
The Self ◽  
Experimental Results ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Global Context ◽  
Overall Performance ◽  
Transformer Model

AbstractSelf-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending to all tokens in the sentence. Despite improvements in performance, recent research argues that the self-attention mechanism tends to concentrate more on the global context with less emphasis on the contextual information available within the local neighbourhood of tokens. This work presents the Dual Contextual (DC) module, an extension of the conventional self-attention unit, to effectively leverage both the local and global contextual information. The goal is to further improve the sentence representation ability of the encoder and decoder subnetworks, thus enhancing the overall performance of the translation model. Experimental results on WMT’14 English-German (En$$\rightarrow $$ → De) and eight IWSLT translation tasks show that the DC module can further improve the translation performance of the Transformer model.

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