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Applying N-gram Alignment Entropy to Improve Feature Decay Algorithms
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 245-256 (2017) (2017)
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322 |
Comparative Human and Automatic Evaluation of Glass-Box and Black-Box Approaches to Interactive Translation Prediction
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 97-108 (2017) (2017)
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323 |
Historical Documents Modernization
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 295-306 (2017) (2017)
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324 |
Maintaining Sentiment Polarity in Translation of User-Generated Content
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 73-84 (2017) (2017)
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325 |
Is Neural Machine Translation the New State of the Art?
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 109-120 (2017) (2017)
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326 |
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 331-342 (2017) (2017)
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327 |
Learning Morphological Normalization for Translation from and into Morphologically Rich Languages
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 49-60 (2017) (2017)
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328 |
Continuous Learning from Human Post-Edits for Neural Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 233-244 (2017) (2017)
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329 |
Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017)
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Abstract:
We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems’ outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1515/pralin-2017-0014 https://doaj.org/article/b4e1fd45807c4747bcc465fbf853507b
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330 |
A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 133-145 (2017) (2017)
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331 |
A Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 159-170 (2017) (2017)
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332 |
Neural Networks Classifier for Data Selection in Statistical Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 283-294 (2017) (2017)
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333 |
Empirical Investigation of Optimization Algorithms in Neural Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 13-25 (2017) (2017)
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334 |
Parallelization of Neural Network Training for NLP with Hogwild!
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In: Prague Bulletin of Mathematical Linguistics , Vol 109, Iss 1, Pp 29-38 (2017) (2017)
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335 |
Neural Monkey: An Open-source Tool for Sequence Learning
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In: Prague Bulletin of Mathematical Linguistics , Vol 107, Iss 1, Pp 5-17 (2017) (2017)
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336 |
Difference between Written and Spoken Czech: The Case of Verbal Nouns Denoting an Action
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In: Prague Bulletin of Mathematical Linguistics , Vol 107, Iss 1, Pp 19-38 (2017) (2017)
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337 |
Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 307-318 (2017) (2017)
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338 |
Learnability and falsifiability of Construction Grammars
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In: Proceedings of the Linguistic Society of America; Vol 2 (2017): Proceedings of the Linguistic Society of America; 1:1–15 ; 2473-8689 (2017)
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339 |
Variation in the pronunciation/silence of the prepositions in locative determiners
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In: Proceedings of the Linguistic Society of America; Vol 2 (2017): Proceedings of the Linguistic Society of America; 22:1–15 ; 2473-8689 (2017)
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