Unsupervised Quality Estimation Without Reference Corpus for Subtitle Machine Translation Using Word Embeddings

2019 ◽  
Author(s):  
Prabhakar Gupta ◽  
Shaktisingh Shekhawat ◽  
Keshav Kumar
Keyword(s):  
Machine Translation ◽  
Word Embeddings ◽  
Quality Estimation ◽  
Reference Corpus

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.

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

scholarly journals Syntax and Semantics in Quality Estimation of Machine Translation

2014 ◽  
Author(s):  
Rasoul Kaljahi ◽  
Jennifer Foster ◽  
Johann Roturier
Keyword(s):  
Machine Translation ◽  
Quality Estimation

scholarly journals Using Word Embeddings to Enforce Document-Level Lexical Consistency in Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 85-96 ◽  
Author(s):  
Eva Martínez Garcia ◽  
Carles Creus ◽  
Cristina España-Bonet ◽  
Lluís Màrquez
Keyword(s):  
Machine Translation ◽  
Evaluation Metrics ◽  
Automatic Evaluation ◽  
Word Embeddings ◽  
Standard Document ◽  
Sentence Level ◽  
Word Translation ◽  
Stochastic Mechanism ◽  
Document Level

Abstract We integrate new mechanisms in a document-level machine translation decoder to improve the lexical consistency of document translations. First, we develop a document-level feature designed to score the lexical consistency of a translation. This feature, which applies to words that have been translated into different forms within the document, uses word embeddings to measure the adequacy of each word translation given its context. Second, we extend the decoder with a new stochastic mechanism that, at translation time, allows to introduce changes in the translation oriented to improve its lexical consistency. We evaluate our system on English–Spanish document translation, and we conduct automatic and manual assessments of its quality. The automatic evaluation metrics, applied mainly at sentence level, do not reflect significant variations. On the contrary, the manual evaluation shows that the system dealing with lexical consistency is preferred over both a standard sentence-level and a standard document-level phrase-based MT systems.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

Quality estimation of machine translation for literature

2018 ◽  
pp. 183-208
Author(s):  
Mozhgan Ghassemiazghandi ◽  
Tengku Sepora Tengku Mahadi
Keyword(s):  
Machine Translation ◽  
Quality Estimation

scholarly journals Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder

2019 ◽  
Vol 33 ◽  
pp. 5466-5473 ◽  
Author(s):  
Yingce Xia ◽  
Tianyu He ◽  
Xu Tan ◽  
Fei Tian ◽  
Di He ◽  
...  
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
State Of The Art ◽  
Compact Model ◽  
Word Embeddings ◽  
Simple Method ◽  
Neural Machine Translation ◽  
German Translation ◽  
One Step ◽  
Target Side

Sharing source and target side vocabularies and word embeddings has been a popular practice in neural machine translation (briefly, NMT) for similar languages (e.g., English to French or German translation). The success of such wordlevel sharing motivates us to move one step further: we consider model-level sharing and tie the whole parts of the encoder and decoder of an NMT model. We share the encoder and decoder of Transformer (Vaswani et al. 2017), the state-of-the-art NMT model, and obtain a compact model named Tied Transformer. Experimental results demonstrate that such a simple method works well for both similar and dissimilar language pairs. We empirically verify our framework for both supervised NMT and unsupervised NMT: we achieve a 35.52 BLEU score on IWSLT 2014 German to English translation, 28.98/29.89 BLEU scores on WMT 2014 English to German translation without/with monolingual data, and a 22.05 BLEU score on WMT 2016 unsupervised German to English translation.

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