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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Historical Documents Modernization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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Abstract:
When translating between a morphologically rich language (MRL) and English, word forms in the MRL often encode grammatical information that is irrelevant with respect to English, leading to data sparsity issues. This problem can be mitigated by removing from the MRL irrelevant information through normalization. Such preprocessing is usually performed in a deterministic fashion, using hand-crafted rules and yielding suboptimal representations. We introduce here a simple way to automatically compute an appropriate normalization of the MRL and show that it can improve machine translation in both directions.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1515/pralin-2017-0008 https://doaj.org/article/284791314bd14a9682336c7b423377e1
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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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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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