[PDF] Top 20 Robust Machine Translation Evaluation with Entailment Features
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Robust Machine Translation Evaluation with Entailment Features
... In this experiment, we predict human pairwise pref- erence judgments (cf. Section 4). We reuse the linear regression framework from Section 2 and predict pairwise preferences by predicting two ab- solute scores (as ... See full document
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Proceedings of the Second Conference on Machine Translation
... three translation tasks: Machine Translation of News, Biomedical Translation, and Multimodal Machine Translation, two evaluation tasks: Metrics and Quality Estimation, as ... See full document
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Comparing a Hand crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation
... automatic evaluation of machine translation (MT) has proven to be a very significant research ...automatic evaluation methods focus on the evalua- tion of the output of MT as they compute ... See full document
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Towards Robust Neural Machine Translation
... Chinese-English NIST datasets trained on RNN- based NMT. Shen et al. (2016) propose minimum risk training (MRT) for NMT, which directly op- timizes model parameters with respect to BLEU scores. Wang et al. (2017) address ... See full document
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Source Language Features and Maximum Correlation Training for Machine Translation Evaluation
... To evaluate our source-sentence based metrics, they are used to evaluate the 7 MT outputs, with the 4 sets of human references. The sentence-level Pearson’s correlation with human judgment is computed for each MT output, ... See full document
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Semantic Role Features for Machine Translation
... role features is not statistically ...accurate evaluation of the sentence’s fluency, which is the property that semantic role features are supposed to ...role features. Our evaluation ... See full document
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IPA and STOUT: Leveraging Linguistic and Source based Features for Machine Translation Evaluation
... they are known to be robust across domains and are usually good indicators of translation qual- ity (Gim´enez and M`arquez, 2007). So, in order to assess the gain achieved with these measures with respect ... See full document
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Human Evaluation of Neural Machine Translation: The Case of Deep Learning
... on features for which more recent MT systems are not likely to produce errors (even though their study was conducted in 2017, Daems et ...human translation, mostly built on Vilar et ...several ... See full document
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The Contribution of Linguistic Features to Automatic Machine Translation Evaluation
... An example of this appears in the test bed NIST05AE which includes a human-aided sys- tem, LinearB (Callison-Burch, 2005). This system produces correct translations whose words do not necessarily overlap with references. ... See full document
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Robust parfda Statistical Machine Translation Results
... We obtain transductive learning results since we use source sentences of the test set to select data. However, decaying only on the source test set features does not necessarily increase diversity on the target ... See full document
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Robust Tuning Datasets for Statistical Machine Translation
... Statistical Machine Translation (SMT) that makes the hyper-parameters of the SMT system more robust with respect to some specific deficiencies of the parameter tuning al- ...and evaluation ... See full document
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Extending Machine Translation Evaluation Metrics with Lexical Cohesion to Document Level
... few evaluation methods explicitly targeting on the quality of a docu- ...MT evaluation measures, based on the Discourse Representation Theory (Kamp and Reyle, 1993), that generate semantic trees to put to- ... See full document
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Urdu to English Machine Translation using Bilingual Evaluation Understudy
... Statistical Machine Translation ...in machine translation between languages with significant word order differences ...Based Machine Translation (EBMT) that translates input text ... See full document
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Machine Translation Evaluation with Textual Entailment Features
... favorite translation deviates considerably from the ref- erence translation in lexical choice, syntactic structure, and word order, for which it is punished by T RAD M T ...propriate translation of ... See full document
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Semantic Evaluation of Machine Translation
... many evaluation metrics of machine translation in use that focus on surface word level suffer from their lack of tolerance of linguistic variance, and the incorporation of linguistic features ... See full document
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LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors
... some translation languages and worse on others, resulting in medium level ...a robust metric in all cases by constructing augmented features and also a concise and independent model without using any ... See full document
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Machine Translationness: Machine-likeness in Machine Translation Evaluation
... MTness instances in the use of words are NO-L2 and STR- CHAR instances; that is, lexical items that are not recog- nised as language words by the intuitive knowledge of na- tive speakers. The typed dependency tree is ... See full document
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MACHINE TRANSLATION: APPROACHES AND EVALUATION
... Corpus is a large collection of text or speech in a language. A dictionary is a description of the vocabulary of a language arranged alphabetically where as; corpus is large and structured set of texts. A corpus may ... See full document
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Evaluation in the ARPA Machine Translation Program: 1993 Methodology
... Evaluation in the ARPA Machine Translation Program 1993 Methodology Evaluation in the ARPA Machine Translation Program John S White, Theresa A O'Connell PRC Inc M c L e a n , VA 22102 1993 Methodology[.] ... See full document
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11,001 New Features for Statistical Machine Translation
... the features already listed, these translation errors all disappear, as demon- strated by examples 4–5 in Figure ...in translation hypotheses that move “X said” or “X asked” away from the be- ginning ... See full document
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