Results 21 to 30 of about 2,688,278 (298)

Sprostowanie niektórych tłumaczeń z tureckiego ws. Nowy przekład dziejopisów tureckich Ignacego Pietraszewskiego – pomiędzy recenzją a translacją

open access: yesAnnales Universitatis Paedagogicae Cracoviensis. Studia Linguistica, 2018
Subject to analysis in this article is the genre status of Ignacy Pietraszewski’s works commenting upon Collectanea by Józef Sękowski. The former work by Pietraszewski is a press edition of Sprostowania niektórych tłumaczeń z tureckiego [Corrections of ...
Jolanta Klimek-Grądzka
doaj   +1 more source

Correggere e tradurre la poesia: il caso del Parisinus Suppl. Gr. 388

open access: yesLexis, 2020
The present work focuses on the peculiarity of the codex Parisinus Suppl. Gr. 388. This manuscript is marked by a 12th-century Latin translation, running above some Greek verses of Theognis’ Elegies and entirely above the poems by Pseudo-Phocylides ...
La Barbera, Paola Carmela
doaj   +1 more source

Kinematic twist-three contributions to pseudo- and quasi-GPDs and translation invariance

open access: yesJournal of High Energy Physics, 2023
We present explicit expressions for the tree-level “kinematic” twist-three contributions to the nucleon matrix elements of gauge-invariant nonlocal quark-antiquark operators which can be used in lattice calculations of generalized parton distributions ...
V. M. Braun
doaj   +1 more source

A7׳ta: Data on a monolingual Arabic parallel corpus for grammar checking

open access: yesData in Brief, 2019
Grammar error correction can be considered as a “translation” problem, such that an erroneous sentence is “translated” into a correct version of the sentence in the same language.
Nora Madi, Hend S. Al-Khalifa
doaj   +1 more source

Correction: Translating p53 into the clinic [PDF]

open access: yesNature Reviews Clinical Oncology, 2011
Nat. Rev. Clin. Oncol. 8, 25–37 (2011); doi:10.1038/nrclinonc.2010.174 A software error resulted in several incorrect citations and two missing references: National Cancer Institute http://seer.cancer.gov/ (2010) and Met, O. et al. Breast Cancer Res. Treat. 125, 395–406 (2011). The errors have been corrected for the HTML and PDF versions of the article.
Chit Fang Cheok   +3 more
openaire   +1 more source

Apology of the Ukrainian Kantiana: once again on how not to evaluate philosophical translations

open access: yesФілософія освіти, 2022
The paper deals with the review of the translation of Kant’s “Critique of Practical Reason”, published by Vitaly Chorny in this magazine last year as a continuation of the review by I. Ivashchenko and V.
Ihor Burkovskyi
doaj   +1 more source

Document-Level Adaptation for Neural Machine Translation

open access: yesNMT@ACL, 2018
It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator’s corrections within the document itself. We focus on
Sachith Sri Ram Kothur   +2 more
semanticscholar   +1 more source

Automated feedback generation for introductory programming assignments [PDF]

open access: yesACM-SIGPLAN Symposium on Programming Language Design and Implementation, 2012
We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that ...
Rishabh Singh   +2 more
semanticscholar   +1 more source

Divergent Polysemy: The Case of Slovene namreč vs. English namely

open access: yesELOPE, 2007
Divergent polysemy is a formidable translation problem; especially in L1-L2 translation. In the paper the case of the Slovene-English pair of items namreč and namely was used to illustrate the most frequent polysemy-triggered translation errors and ...
Primož Jurko
doaj   +1 more source

Continuous Learning from Human Post-Edits for Neural Machine Translation

open access: yesPrague Bulletin of Mathematical Linguistics, 2017
Improving machine translation (MT) by learning from human post-edits is a powerful solution that is still unexplored in the neural machine translation (NMT) framework.
Marco Turchi   +3 more
semanticscholar   +1 more source

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