Statistical MT Training for the translation of English-Arabic UN Resolutions

2020 ◽  
Author(s):  
Asma AlOtaibi
Keyword(s):  
Statistical Mt

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

Function words in statistical machine-translated Chinese and original Chinese: A study into the translationese of machine translation systems

2018 ◽  
Vol 34 (4) ◽  
pp. 752-771
Author(s):  
Chen-li Kuo
Keyword(s):  
Machine Translation ◽  
Attribute Selection ◽  
Close Attention ◽  
Function Words ◽  
Rule Based ◽  
Source Language ◽  
Statistical Mt ◽  
Chinese Texts ◽  
Translation Systems

Abstract Statistical approaches have become the mainstream in machine translation (MT), for their potential in producing less rigid and more natural translations than rule-based approaches. However, on closer examination, the uses of function words between statistical machine-translated Chinese and the original Chinese are different, and such differences may be associated with translationese as discussed in translation studies. This article examines the distribution of Chinese function words in a comparable corpus consisting of MTs and the original Chinese texts extracted from Wikipedia. An attribute selection technique is used to investigate which types of function words are significant in discriminating between statistical machine-translated Chinese and the original texts. The results show that statistical MT overuses the most frequent function words, even when alternatives exist. To improve the quality of the end product, developers of MT should pay close attention to modelling Chinese conjunctions and adverbial function words. The results also suggest that machine-translated Chinese shares some characteristics with human-translated texts, including normalization and being influenced by the source language; however, machine-translated texts do not exhibit other characteristics of translationese such as explicitation.

Improving statistical MT by coupling reordering and decoding

2007 ◽  
Vol 20 (3) ◽  
pp. 199-215 ◽  
Author(s):  
Josep Maria Crego ◽  
José B. Mariño
Keyword(s):  
Statistical Mt

scholarly journals Domain Adaptation for Machine Translation with Instance Selection

2015 ◽  
Vol 103 (1) ◽  
pp. 5-20 ◽  
Author(s):  
Ergun Biçici
Keyword(s):  
Machine Translation ◽  
Domain Adaptation ◽  
Test Sample ◽  
Instance Selection ◽  
Test Set ◽  
Sample Distribution ◽  
Sampling Process ◽  
Statistical Mt ◽  
Research Questions ◽  
And Training

Abstract Domain adaptation for machine translation (MT) can be achieved by selecting training instances close to the test set from a larger set of instances. We consider 7 different domain adaptation strategies and answer 7 research questions, which give us a recipe for domain adaptation in MT. We perform English to German statistical MT (SMT) experiments in a setting where test and training sentences can come from different corpora and one of our goals is to learn the parameters of the sampling process. Domain adaptation with training instance selection can obtain 22% increase in target 2-gram recall and can gain up to 3:55 BLEU points compared with random selection. Domain adaptation with feature decay algorithm (FDA) not only achieves the highest target 2-gram recall and BLEU performance but also perfectly learns the test sample distribution parameter with correlation 0:99. Moses SMT systems built with FDA selected 10K training sentences is able to obtain F1 results as good as the baselines that use up to 2M sentences. Moses SMT systems built with FDA selected 50K training sentences is able to obtain F1 point better results than the baselines.

scholarly journals Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments

2019 ◽  
Vol 45 (3) ◽  
pp. 515-558
Author(s):  
Marina Fomicheva ◽  
Lucia Specia
Keyword(s):  
Evaluation Study ◽  
Evaluation Metrics ◽  
Local Dependency ◽  
Translation Quality ◽  
Global Correlation ◽  
Mt Evaluation ◽  
Statistical Mt ◽  
Wide Range ◽  
The Difference ◽  
Different Levels

Automatic Machine Translation (MT) evaluation is an active field of research, with a handful of new metrics devised every year. Evaluation metrics are generally benchmarked against manual assessment of translation quality, with performance measured in terms of overall correlation with human scores. Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments. However, little insight has been provided regarding the weaknesses and strengths of existing approaches and their behavior in different settings. In this work we conduct a broad meta-evaluation study of the performance of a wide range of evaluation metrics focusing on three major aspects. First, we analyze the performance of the metrics when faced with different levels of translation quality, proposing a local dependency measure as an alternative to the standard, global correlation coefficient. We show that metric performance varies significantly across different levels of MT quality: Metrics perform poorly when faced with low-quality translations and are not able to capture nuanced quality distinctions. Interestingly, we show that evaluating low-quality translations is also more challenging for humans. Second, we show that metrics are more reliable when evaluating neural MT than the traditional statistical MT systems. Finally, we show that the difference in the evaluation accuracy for different metrics is maintained even if the gold standard scores are based on different criteria.

scholarly journals Is Neural Machine Translation the New State of the Art?

2017 ◽  
Vol 108 (1) ◽  
pp. 109-120 ◽  
Author(s):  
Sheila Castilho ◽  
Joss Moorkens ◽  
Federico Gaspari ◽  
Iacer Calixto ◽  
John Tinsley ◽  
...  
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Evaluation Methods ◽  
Automatic Evaluation ◽  
New Paradigm ◽  
Neural Machine Translation ◽  
Human Evaluation ◽  
Statistical Mt

Abstract This paper discusses neural machine translation (NMT), a new paradigm in the MT field, comparing the quality of NMT systems with statistical MT by describing three studies using automatic and human evaluation methods. Automatic evaluation results presented for NMT are very promising, however human evaluations show mixed results. We report increases in fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to oversell.

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