A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory

2021 ◽  
pp. 1-15
Author(s):  
Yidong Chen ◽  
Enjun Zhong ◽  
Yiqi Tong ◽  
Yanru Qiu ◽  
Xiaodong Shi
Keyword(s):  
Machine Translation ◽  
Estimation Model ◽  
Quality Estimation ◽  
Translation Quality ◽  
Model Based ◽  
Centering Theory ◽  
Document Level

scholarly journals Improving Evaluation of Document-level Machine Translation Quality Estimation

2017 ◽  
Author(s):  
Yvette Graham ◽  
Qingsong Ma ◽  
Timothy Baldwin ◽  
Qun Liu ◽  
Carla Parra ◽  
...  
Keyword(s):  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Document Level

A Survey on Document-level Neural Machine Translation

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Sameen Maruf ◽  
Fahimeh Saleh ◽  
Gholamreza Haffari
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Research Field ◽  
Translation Process ◽  
Future Directions ◽  
Translation Quality ◽  
Current State ◽  
Evaluation Strategies ◽  
Almost All ◽  
Document Level

Machine translation (MT) is an important task in natural language processing (NLP), as it automates the translation process and reduces the reliance on human translators. With the resurgence of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques for most language-pairs. Up until a few years ago, almost all of the neural translation models translated sentences independently , without incorporating the wider document-context and inter-dependencies among the sentences. The aim of this survey article is to highlight the major works that have been undertaken in the space of document-level machine translation after the neural revolution, so researchers can recognize the current state and future directions of this field. We provide an organization of the literature based on novelties in modelling and architectures as well as training and decoding strategies. In addition, we cover evaluation strategies that have been introduced to account for the improvements in document MT, including automatic metrics and discourse-targeted test sets. We conclude by presenting possible avenues for future exploration in this research field.

Dimensionality reduction methods for machine translation quality estimation

2013 ◽  
Vol 27 (3-4) ◽  
pp. 281-301 ◽  
Author(s):  
Jesús González-Rubio ◽  
J. Ramón Navarro-Cerdán ◽  
Francisco Casacuberta
Keyword(s):  
Dimensionality Reduction ◽  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Reduction Methods

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 UAlacant word-level machine translation quality estimation system at WMT 2015

2015 ◽  
Author(s):  
Miquel Esplà-Gomis ◽  
Felipe Sánchez-Martínez ◽  
Mikel Forcada
Keyword(s):  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Word Level ◽  
Estimation System

Online Multitask Learning for Machine Translation Quality Estimation

2015 ◽  
Author(s):  
José G. C. de Souza ◽  
Matteo Negri ◽  
Elisa Ricci ◽  
Marco Turchi
Keyword(s):  
Machine Translation ◽  
Multitask Learning ◽  
Quality Estimation ◽  
Translation Quality

Unsupervised Machine Translation Quality Estimation in Black-Box Setting

2020 ◽  
pp. 24-36
Author(s):  
Hui Huang ◽  
Hui Di ◽  
Jin’an Xu ◽  
Kazushige Ouchi ◽  
Yufeng Chen
Keyword(s):  
Machine Translation ◽  
Black Box ◽  
Quality Estimation ◽  
Translation Quality

Unsupervised Translation Quality Estimation for Digital Entertainment Content Subtitles

2020 ◽  
Vol 14 (01) ◽  
pp. 137-151
Author(s):  
Prabhakar Gupta ◽  
Mayank Sharma
Keyword(s):  
Machine Translation ◽  
Experimental Results ◽  
Parallel Translation ◽  
Word Embeddings ◽  
Quality Estimation ◽  
Neural Machine Translation ◽  
Intensity Index ◽  
Translation Quality ◽  
Digital Entertainment ◽  
Reference Corpus

We demonstrate the potential for using aligned bilingual word embeddings in developing an unsupervised method to evaluate machine translations without a need for parallel translation corpus or reference corpus. We explain different aspects of digital entertainment content subtitles. We share our experimental results for four languages pairs English to French, German, Portuguese, Spanish, and present findings on the shortcomings of Neural Machine Translation for subtitles. We propose several improvements over the system designed by Gupta et al. [P. Gupta, S. Shekhawat and K. Kumar, Unsupervised quality estimation without reference corpus for subtitle machine translation using word embeddings, IEEE 13th Int. Conf. Semantic Computing, 2019, pp. 32–38.] by incorporating custom embedding model curated to subtitles, compound word splits and punctuation inclusion. We show a massive run time improvement of the order of [Formula: see text] by considering three types of edits, removing Proximity Intensity Index (PII) and changing post-edit score calculation from their system.

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