NiuTrans Submission for CCMT19 Quality Estimation Task

2019 ◽  
pp. 82-92
Author(s):  
Ziyang Wang ◽  
Hui Liu ◽  
Hexuan Chen ◽  
Kai Feng ◽  
Zeyang Wang ◽  
...  
Keyword(s):  
Quality Estimation ◽  
Estimation Task

scholarly journals Alibaba Submission for WMT18 Quality Estimation Task

2018 ◽  
Author(s):  
Jiayi Wang ◽  
Kai Fan ◽  
Bo Li ◽  
Fengming Zhou ◽  
Boxing Chen ◽  
...  
Keyword(s):  
Quality Estimation ◽  
Estimation Task

Tencent Submissions for the CCMT 2020 Quality Estimation Task

2020 ◽  
pp. 123-131
Author(s):  
Zixuan Wang ◽  
Haijiang Wu ◽  
Qingsong Ma ◽  
Xinjie Wen ◽  
Ruichen Wang ◽  
...  
Keyword(s):  
Quality Estimation ◽  
Estimation Task

NJUNLP’s Submission for CCMT20 Quality Estimation Task

2020 ◽  
pp. 114-122
Author(s):  
Qu Cui ◽  
Xiang Geng ◽  
Shujian Huang ◽  
Jiajun Chen
Keyword(s):  
Quality Estimation ◽  
Estimation Task

SAU’S Submission for CCMT 2021 Quality Estimation Task

2021 ◽  
pp. 16-24
Author(s):  
Yanan Li ◽  
Na Ye ◽  
Dongfeng Cai
Keyword(s):  
Quality Estimation ◽  
Estimation Task

scholarly journals Language Adaptation for Extending Post-Editing Estimates for Closely Related Languages

2016 ◽  
Vol 106 (1) ◽  
pp. 181-192 ◽  
Author(s):  
Miguel Rios ◽  
Serge Sharoff
Keyword(s):  
Open Source ◽  
Transfer Learning ◽  
Feature Representation ◽  
Romance Languages ◽  
Quality Estimation ◽  
Estimation Task ◽  
Training Resources ◽  
New Feature ◽  
The Moment ◽  
Estimation Models

Abstract This paper presents an open-source toolkit for predicting human post-editing efforts for closely related languages. At the moment, training resources for the Quality Estimation task are available for very few language directions and domains. Available resources can be expanded on the assumption that MT errors and the amount of post-editing required to correct them are comparable across related languages, even if the feature frequencies differ. In this paper we report a toolkit for achieving language adaptation, which is based on learning new feature representation using transfer learning methods. In particular, we report performance of a method based on Self-Taught Learning which adapts the English-Spanish pair to produce Quality Estimation models for translation from English into Portuguese, Italian and other Romance languages using the publicly available Autodesk dataset.

BJTU’s Submission to CCMT 2020 Quality Estimation Task

2020 ◽  
pp. 105-113
Author(s):  
Hui Huang ◽  
Jin’an Xu ◽  
Wenjing Zhu ◽  
Yufeng Chen ◽  
Rui Dang
Keyword(s):  
Quality Estimation ◽  
Estimation Task

Under- and Over-Estimation

2011 ◽  
Vol 58 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Virginie Crollen ◽  
Julie Castronovo ◽  
Xavier Seron
Keyword(s):  
Empirical Data ◽  
Accurate Estimation ◽  
Numerical Estimation ◽  
Production Task ◽  
Estimation Task ◽  
Reproduction Task ◽  
Perception Task ◽  
Adult Participants ◽  
Mapping Hypothesis ◽  
Task Perception

Over the last 30 years, numerical estimation has been largely studied. Recently, Castronovo and Seron (2007) proposed the bi-directional mapping hypothesis in order to account for the finding that dependent on the type of estimation task (perception vs. production of numerosities), reverse patterns of performance are found (i.e., under- and over-estimation, respectively). Here, we further investigated this hypothesis by submitting adult participants to three types of numerical estimation task: (1) a perception task, in which participants had to estimate the numerosity of a non-symbolic collection; (2) a production task, in which participants had to approximately produce the numerosity of a symbolic numerical input; and (3) a reproduction task, in which participants had to reproduce the numerosity of a non-symbolic numerical input. Our results gave further support to the finding that different patterns of performance are found according to the type of estimation task: (1) under-estimation in the perception task; (2) over-estimation in the production task; and (3) accurate estimation in the reproduction task. Moreover, correlation analyses revealed that the more a participant under-estimated in the perception task, the more he/she over-estimated in the production task. We discussed these empirical data by showing how they can be accounted by the bi-directional mapping hypothesis ( Castronovo & Seron, 2007 ).

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