[PDF] Top 20 Class error rates for evaluation of machine translation output
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Class error rates for evaluation of machine translation output
... promising error categories are lexical and reorder- ing ...best error classes for English are in deed lexical and reordering errors, however for the non-English outputs the inflectional errors and missing ... See full document
5
Blast: A Tool for Error Analysis of Machine Translation Output
... B LAST is a tool for human annotations of bilingual material. Its main purpose is error analysis for ma- chine translation. B LAST is designed for use in any MT evaluation project. It is not tied to ... See full document
6
On the practice of error analysis for machine translation evaluation
... assess machine translation output in qualitative terms, which can be used as a basis for the generation of error profiles for different ...to evaluation it runs the risk of low ... See full document
6
Controlling the Reading Level of Machine Translation Output
... in machine translation and NLP has focused on readability assessment and text simpli- ...system output with standard ...system output but with human ...adopted evaluation metrics for ... See full document
11
Towards Automatic Error Analysis of Machine Translation Output
... enable evaluation of translation output without sentence boundaries ...(e.g., translation of speech recognition ...the output and the ...automatic evaluation in which ... See full document
32
Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... and Machine Translation (MT) make use of the knowledge and expertise of professional translators and interpreters in order to build and improve models for automatic translation or for developing more ... See full document
10
Error Analysis of Statistical Machine Translation Output
... automatic translation output is a difficult ...Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing ... See full document
6
Human Evaluation of Neural Machine Translation: The Case of Deep Learning
... NMT output and a human translation, mostly built on Vilar et ...an error typology and even acknowledged that five categories out of six are directly inspired by their ...These error types can ... See full document
11
Grammatical Error Correction: Machine Translation and Classifiers
... compile a list of patterns using CoNLL training data. We also use an off-the-shelf speller (Flor, 2012; Flor and Futagi, 2012). Results are shown in Table 12. Performance improves by almost 5 and 7 points for the ... See full document
11
Empirical Lower Bounds on Aligment Error Rates in Syntax Based Machine Translation
... In machine translation, as in all other branches of computer science, there is a trade-off between ex- pressivity and ...syntax-based translation systems, occur frequently in hand-aligned parallel ... See full document
9
Comparing a Hand crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation
... word error rate (WER), a lexical similarity ...the output similar to refer- ence ...automatic translation and user-provided translation corrections gathered through an online MT ... See full document
9
A fluency error categorization scheme to guide automated machine translation evaluation
... MT evaluation methods often require expert hu- man ...MT evaluation system, intended for use by post-editors, purchasers and developers, that requires nothing but the raw MT ...MT output, to enable ... See full document
11
Direct Error Rate Minimization for Statistical Machine Translation
... which takes source sentences and a set of weights as inputs, and outputs the evaluation score (e.g., BLEU score) computed on the decoded sentences. Since it is impractical to calculate derivatives of this func- ... See full document
12
Grammatical error correction using neural machine translation
... From the results in Table 1, we can see that NMT- based systems alone are not able to achieve compa- rable results to an SMT-based system. It is proba- bly because of the rare word problem, as increasing the source side ... See full document
7
An Evaluation of Output Quality of Machine Translation Program
... each translation was 10 points: 5 for accuracy and 5 for ...Every translation lost points according to the nature and number of the errors it ...an error at ‘referential meaning at sentence level’, ... See full document
5
Creating a Corpus for Russian Data to Text Generation Using Neural Machine Translation and Post Editing
... 2017) 4 to train a bidirectional encoder-decoder model with attention (Luong et al., 2015). A single-layer LSTM (Hochreiter and Schmidhuber, 1997) is used for both encoder and decoder. We trained using full vocabulary ... See full document
6
Evaluation of Machine Translation
... EVALUATION OF MACHINE TRANSLATION E V A L U A T I O N O F M A C H I N E T R A N S L A T I O N John S White, Theresa A O'Connell P R C Inc M c L e a n , V A 2 2 1 0 2 a n d Lynn M Carlson D o D A B S T[.] ... See full document
5
Phrase Based Evaluation for Machine Translation
... Phrase information has been used in several evaluation methods. In the work of Giménez and Màrquez (2007), overlapping is calculated on the set of words within a same phrase type, and sequences of phrase types are ... See full document
10
Translation Shared Task on Statistical Machine Translation: A Comparison of the Systems Output
... Statistical Machine Translation proposed a translation shared task focused on European language ...to machine translation, that is rule-based, statistical, example-based and ... See full document
7
Confidence based Rewriting of Machine Translation Output
... best translation in their search ...SMT output, and approached the problem as one of second-pass translation be- tween automatic predictions and correct transla- tions (Simard et ... See full document
12
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