Predicting Translation Performance with Referential Translation Machines

Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3 rd system re-sults for German to English sentence-level prediction of translation quality and the 2 nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character n -grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.

• Publication year

  2017

• Venue

  Conference on Machine Translation

• Publication date

  2017-09-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/v1/W17-4759
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity

  2017cited by this paper
• RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity

  2017cited by this paper
• The UU Submission to the Machine Translation Quality Estimation Task

  2016cited by this paper
• YSDA Participation in the WMT’16 Quality Estimation Shared Task

  2016cited by this paper
• Referential Translation Machines for Predicting Translation Performance

  2016influential reference
• ParFDA for Instance Selection for Statistical Machine Translation

  2016influential reference
• Referential translation machines for predicting semantic similarity

  2015cited by this paper
• chrF: character n-gram F-score for automatic MT evaluation

  2015cited by this paper
• Scalable Modified Kneser-Ney Language Model Estimation

  2013cited by this paper
• Findings of the 2012 Workshop on Statistical Machine Translation

  2012cited by this paper
• The Regression Model of Machine Translation

  2012cited by this paper
• Unsupervised and Transfer Learning - Workshop held at ICML 2011, Bellevue, Washington, USA, July 2, 2011

  2012cited by this paper
• Moses: Open Source Toolkit for Statistical Machine Translation

  2007cited by this paper
• Extremely randomized trees

  2006influential reference
• A Study of Translation Edit Rate with Targeted Human Annotation

  2006cited by this paper
• A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION

  2005cited by this paper
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction

  2004cited by this paper
• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms

  2002cited by this paper
• Gene Selection for Cancer Classification using Support Vector Machines

  2002cited by this paper
• A decision-theoretic generalization of on-line learning and an application to boosting

  1997influential reference
• The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses

  1984influential reference

• Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines

  2024influential citation
• Machine Translation Performance Prediction System: Optimal Prediction for Optimal Translation

  2022influential citation
• Keep It or Not: Word Level Quality Estimation for Post-Editing

  2018cites this paper
• RTM results for Predicting Translation Performance

  2018cites this paper
• Findings of the WMT 2018 Shared Task on Quality Estimation

  2018cites this paper
• Assessing Quality Estimation Models for Sentence-Level Prediction

  2018cites this paper
• SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

  2017cites this paper
• Findings of the 2017 Conference on Machine Translation (WMT17)

  2017cites this paper