Investigating Continuous Space Language Models for Machine Translation Quality Estimation

We present novel features designed with a deep neural network for Machine Translation (MT) Quality Estimation (QE). The features are learned with a Continuous Space Language Model to estimate the probabilities of the source and target segments. These new features, along with standard MT system-independent features, are benchmarked on a series of datasets with various quality labels, including postediting effort, human translation edit rate, post-editing time and METEOR. Results show significant improvements in prediction over the baseline, as well as over systems trained on state of the art feature sets for all datasets. More notably, the addition of the newly proposed features improves over the best QE systems in WMT12 and WMT14 by a significant margin.

• Publication year

  2015

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  Unknown publication date

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/v1/D15-1125
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics

  2015cited by this paper
• A Bayesian non-linear method for feature selection in machine translation quality estimation

  2015cited by this paper
• The USFD Spoken Language Translation System for IWSLT 2014

  2015cited by this paper
• Adequacy–Fluency Metrics: Evaluating MT in the Continuous Space Model Framework

  2015cited by this paper
• Efficient training strategies for deep neural network language models.

  2014cited by this paper
• Crowdsourcing High-Quality Parallel Data Extraction from Twitter

  2014cited by this paper
• QuEst - A translation quality estimation framework

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

  2013cited by this paper
• QuEst – Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation

  2013cited by this paper
• Linguistic Regularities in Continuous Space Word Representations

  2013cited by this paper
• An Investigation on the Effectiveness of Features for Translation Quality Estimation

  2013influential reference
• Deep Neural Network Language Models

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

  2012cited by this paper
• Continuous Space Translation Models for Phrase-Based Statistical Machine Translation

  2012cited by this paper
• Intelligent Selection of Language Model Training Data

  2010cited by this paper
• Continuous space language models

  2007cited by this paper
• Training Neural Network Language Models on Very Large Corpora

  2005cited by this paper
• METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

  2005cited by this paper
• A Neural Probabilistic Language Model

  2003cited by this paper

• Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation

  2025cites this paper
• From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation

  2024cites this paper
• Deep Learning for Natural Language Processing: A Survey

  2023cites this paper
• Quality Estimation for Machine Translation with Multi-granularity Interaction

  2020cites this paper
• Analyzing Quality Estimation Of English-Hindi Machine Translation System

  2020cites this paper
• NJU Submissions for the WMT19 Quality Estimation Shared Task

  2019cites this paper
• Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech’s Participation in the WMT 2018 Quality Estimation Task

  2018cites this paper
• A Unified Neural Network for Quality Estimation of Machine Translation

  2018cites this paper
• Improving Machine Translation Quality Estimation with Neural Network Features

  2017cites this paper
• Neural Post-Editing Based on Quality Estimation

  2017cites this paper
• Document-level machine translation quality estimation

  2016cites this paper
• SimpleNets: Quality Estimation with Resource-Light Neural Networks

  2016cites this paper
• Dieses Modell erweitert das " Multi-Layer Perceptron " von

  2016cites this paper
• SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features

  2016cites this paper