Towards Automatic Error Analysis of Machine Translation Output
نویسندگان
چکیده
منابع مشابه
Towards Automatic Error Analysis of Machine Translation Output
Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards developmen...
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Evaluation of automatic translation output is a difficult task. Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system. However the interpretation of all of these measures is not at all clear, and the identification o...
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Future improvement of machine translation systems requires reliable automatic evaluation and error classification measures to avoid time and money consuming human classification. In this article, we propose a new method for automatic error classification and systematically compare its results to those obtained by humans. We show that the proposed automatic measures correlate well with human jud...
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We present BLAST, an open source tool for error analysis of machine translation (MT) output. We believe that error analysis, i.e., to identify and classify MT errors, should be an integral part of MT development, since it gives a qualitative view, which is not obtained by standard evaluation methods. BLAST can aid MT researchers and users in this process, by providing an easy-to-use graphical u...
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Evaluation of machine translation output is an important but difficult task. Over the last years, a variety of automatic evaluation measures have been studied, some of them like Word Error Rate (WER), Position Independent Word Error Rate (PER) and BLEU and NIST scores have become widely used tools for comparing different systems as well as for evaluating improvements within one system. However,...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2011
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli_a_00072