Predicting Human Translation Quality
نویسندگان
چکیده
We present a first attempt at predicting the quality of translations produced by human, professional translators. We examine datasets annotated for quality at sentenceand word-level for four language pairs and provide experiments with prediction models for these datasets. We compare the performance of such models against that of models built from machine translations, highlighting a number of challenges in estimating quality and detecting errors in human translations.
منابع مشابه
The Correlation of Machine Translation Evaluation Metrics with Human Judgement on Persian Language
Machine Translation Evaluation Metrics (MTEMs) are the central core of Machine Translation (MT) engines as they are developed based on frequent evaluation. Although MTEMs are widespread today, their validity and quality for many languages is still under question. The aim of this research study was to examine the validity and assess the quality of MTEMs from Lexical Similarity set on machine tra...
متن کاملIntegrating Meaning into Quality Evaluation of Machine Translation
Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiment...
متن کاملEstimating the Quality of Translated User-Generated Content
Previous research on quality estimation for machine translation has demonstrated the possibility of predicting the translation quality of well-formed data. We present a first study on estimating the translation quality of user-generated content. Our dataset contains English technical forum comments which were translated into French by three automatic systems. These translations were rated in te...
متن کاملSyntactic Category Prediction for Improving Translation Quality in English-Korean Machine Translation
This paper proposes the syntactic category prediction for improving translation quality. In parsing using sentence segmentation, the segments are separately parsed and then the parsing results of each segment are combined to generate a global sentence structure. The syntactic category prediction guides the parser to identify relationships among segments and to select the correct parsing results...
متن کاملPredicting Crowd-Based Translation Quality with Language-Independent Feature Vectors
Research over the past years has shown that machine translation results can be greatly enhanced with the help of monoor bilingual human contributors, e.g. by asking humans to proofread or correct outputs of machine translation systems. However, it remains difficult to determine the quality of individual revisions. This paper proposes a method to determine the quality of individual contributions...
متن کامل