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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...13 14 15 16 17 Hits 321 – 339 of 339 321 Applying N-gram Alignment Entropy to Improve Feature Decay Algorithms Poncelas Alberto; Maillette de Buy Wenniger Gideon; Way Andy In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 245-256 (2017) (2017) BASE Show details 322 Comparative Human and Automatic Evaluation of Glass-Box and Black-Box Approaches to Interactive Translation Prediction Torregrosa Daniel; Pérez-Ortiz Juan Antonio; Forcada Mikel L. In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 97-108 (2017) (2017) BASE Show details 323 Historical Documents Modernization Domingo Miguel; Chinea-Rios Mara; Casacuberta Francisco In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 295-306 (2017) (2017) BASE Show details 324 Maintaining Sentiment Polarity in Translation of User-Generated Content Lohar Pintu; Afli Haithem; Way Andy In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 73-84 (2017) (2017) Abstract: The advent of social media has shaken the very foundations of how we share information, with Twitter, Facebook, and Linkedin among many well-known social networking platforms that facilitate information generation and distribution. However, the maximum 140-character restriction in Twitter encourages users to (sometimes deliberately) write somewhat informally in most cases. As a result, machine translation (MT) of user-generated content (UGC) becomes much more difficult for such noisy texts. In addition to translation quality being affected, this phenomenon may also negatively impact sentiment preservation in the translation process. That is, a sentence with positive sentiment in the source language may be translated into a sentence with negative or neutral sentiment in the target language. In this paper, we analyse both sentiment preservation and MT quality per se in the context of UGC, focusing especially on whether sentiment classification helps improve sentiment preservation in MT of UGC. We build four different experimental setups for tweet translation (i) using a single MT model trained on the whole Twitter parallel corpus, (ii) using multiple MT models based on sentiment classification, (iii) using MT models including additional out-of-domain data, and (iv) adding MT models based on the phrase-table fill-up method to accompany the sentiment translation models with an aim of improving MT quality and at the same time maintaining sentiment polarity preservation. Our empirical evaluation shows that despite a slight deterioration in MT quality, our system significantly outperforms the Baseline MT system (without using sentiment classification) in terms of sentiment preservation. We also demonstrate that using an MT engine that conveys a sentiment different from that of the UGC can even worsen both the translation quality and sentiment preservation. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/e0f59b17b6224cb18d537c99ffa4bde7 https://doi.org/10.1515/pralin-2017-0010 BASE Hide details 325 Is Neural Machine Translation the New State of the Art? Castilho Sheila; Moorkens Joss; Gaspari Federico... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 109-120 (2017) (2017) BASE Show details 326 Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English Ataman Duygu; Negri Matteo; Turchi Marco... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 331-342 (2017) (2017) BASE Show details 327 Learning Morphological Normalization for Translation from and into Morphologically Rich Languages Burlot Franck; Yvon François In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 49-60 (2017) (2017) BASE Show details 328 Continuous Learning from Human Post-Edits for Neural Machine Translation Turchi Marco; Negri Matteo; Farajian M. Amin... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 233-244 (2017) (2017) BASE Show details 329 Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation Klubička Filip; Toral Antonio; Sánchez-Cartagena Víctor M. In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017) BASE Show details 330 A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation Tezcan Arda; Hoste Véronique; Macken Lieve In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 133-145 (2017) (2017) BASE Show details 331 A Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines Burchardt Aljoscha; Macketanz Vivien; Dehdari Jon... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 159-170 (2017) (2017) BASE Show details 332 Neural Networks Classifier for Data Selection in Statistical Machine Translation Peris Álvaro; Chinea-Ríos Mara; Casacuberta Francisco In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 283-294 (2017) (2017) BASE Show details 333 Empirical Investigation of Optimization Algorithms in Neural Machine Translation Bahar Parnia; Alkhouli Tamer; Peter Jan-Thorsten... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 13-25 (2017) (2017) BASE Show details 334 Parallelization of Neural Network Training for NLP with Hogwild! Deyringer Valentin; Fraser Alexander; Schmid Helmut... In: Prague Bulletin of Mathematical Linguistics , Vol 109, Iss 1, Pp 29-38 (2017) (2017) BASE Show details 335 Neural Monkey: An Open-source Tool for Sequence Learning Helcl Jindřich; Libovický Jindřich In: Prague Bulletin of Mathematical Linguistics , Vol 107, Iss 1, Pp 5-17 (2017) (2017) BASE Show details 336 Difference between Written and Spoken Czech: The Case of Verbal Nouns Denoting an Action Kolářová Veronika; Kolář Jan; Mikulová Marie In: Prague Bulletin of Mathematical Linguistics , Vol 107, Iss 1, Pp 19-38 (2017) (2017) BASE Show details 337 Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features Avramidis Eleftherios In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 307-318 (2017) (2017) BASE Show details 338 Learnability and falsifiability of Construction Grammars Dunn, Jonathan In: Proceedings of the Linguistic Society of America; Vol 2 (2017): Proceedings of the Linguistic Society of America; 1:1–15 ; 2473-8689 (2017) BASE Show details 339 Variation in the pronunciation/silence of the prepositions in locative determiners Di Sciullo, Anna Maria In: Proceedings of the Linguistic Society of America; Vol 2 (2017): Proceedings of the Linguistic Society of America; 22:1–15 ; 2473-8689 (2017) BASE Show details Page: 1...13 14 15 16 17

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