A Quality Estimation System for Hungarian

2018 ◽  
pp. 201-213
Author(s):  
Zijian Győző Yang ◽  
Andrea Dömötör ◽  
László János Laki
Keyword(s):  
Quality Estimation ◽  
Estimation System

scholarly journals Pushing the Limits of Translation Quality Estimation

2017 ◽  
Vol 5 ◽  
pp. 205-218 ◽  
Author(s):  
André F. T. Martins ◽  
Marcin Junczys-Dowmunt ◽  
Fabio N. Kepler ◽  
Ramón Astudillo ◽  
Chris Hokamp ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Neural Model ◽  
Quality Estimation ◽  
Translation Quality ◽  
Word Level ◽  
Current State ◽  
Sentence Level ◽  
Feature Based ◽  
Human Effort ◽  
Estimation System

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).

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

scholarly journals Quality Estimation System for Resistance Spot Welding of Stainless Steel

2019 ◽  
Vol 59 (11) ◽  
pp. 2073-2076 ◽  
Author(s):  
Jing Wen ◽  
Hong De Jia ◽  
Chun Sheng Wang
Keyword(s):  
Stainless Steel ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Quality Estimation ◽  
Resistance Spot ◽  
Estimation System

Quality Estimation of the Resistance Spot Welding Based on Hopfield Network

2011 ◽  
Vol 189-193 ◽  
pp. 3364-3369
Author(s):  
Hong Jie Zhang ◽  
Yan Yan Hou
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Fuzzy Theory ◽  
Welding Process ◽  
Hopfield Network ◽  
Quality Estimation ◽  
Resistance Spot ◽  
Displacement Signal ◽  
Electrode Displacement ◽  
Estimation System

Lots of dynamic information, which can directly or indirectly reflect the quality of welded spot, is included within the electrode displacement signal of resistance spot welding process. In this research, the displacement signal is monitored and mapped into a 15×25 element bipolarized matrix by means of some method of fuzzy theory. Some welded spots are classified into five classes according to the prototype pattern matrixes. An effective RSW quality estimation system is developed based on Hopfield network when taking the tensile shear strength of the welded spot joint as the estimation index of welded spot quality. The results of cross-validation test shows that the Hopfield network can satisfactorily accomplish the task of classification of the welded spot and has board application prospects.

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

2009 ◽  
Vol 43 (28) ◽  
pp. 4303-4310 ◽  
Author(s):  
Hui Li ◽  
Fazlay Faruque ◽  
Worth Williams ◽  
Mohammad Al-Hamdan ◽  
Jeffrey Luvall ◽  
...  
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Temporal Scale ◽  
Quality Estimation ◽  
Estimation System

scholarly journals A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 133-145 ◽  
Author(s):  
Arda Tezcan ◽  
Véronique Hoste ◽  
Lieve Macken
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Network Architecture ◽  
Statistical Machine Translation ◽  
Neural Network Architecture ◽  
Quality Estimation ◽  
Grammatical Errors ◽  
Estimation System ◽  
Syntactic Context

Abstract In this paper we present a Neural Network (NN) architecture for detecting grammatical errors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word representations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting overall post-editing effort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting overall post-editing effort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages.

scholarly journals QUES: A Quality Estimation System of Arabic to English Translation

2020 ◽  
Vol 11 (7) ◽  
Author(s):  
Manar Salamah Ali ◽  
Anfal Alatawi ◽  
Bayader Alsahafi ◽  
Najwa Noorwali
Keyword(s):  
English Translation ◽  
Quality Estimation ◽  
Estimation System
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