Incorporating Human Translator Style into English-Turkish Literary Machine Translation

Although machine translation systems are mostly designed to serve in the general domain, there is a growing tendency to adapt these systems to other domains like literary translation. In this paper, we focus on English-Turkish literary translation and develop machine translation models that take into account the stylistic features of translators. We fine-tune a pre-trained machine translation model by the manually-aligned works of a particular translator. We make a detailed analysis of the effects of manual and automatic alignments, data augmentation methods, and corpus size on the translations. We propose an approach based on stylistic features to evaluate the style of a translator in the output translations. We show that the human translator style can be highly recreated in the target machine translations by adapting the models to the style of the translator

Creación y Simulación de Metodologías de Análisis, Clasificación e Integración de Nuevos Requerimientos a Software Propietario

La priorización de nuevos requerimientos a implementar en un software propietario es un punto fundamental para su mantenimiento, la conservación de la calidad, observación de las reglas de negocio y los estándares de la empresa. Aunque existen herramientas de priorización basadas en técnicas probadas y reconocidas, las mismas requieren una calificación previa de cada requerimiento. Si la empresa cuenta con solicitudes provenientes de varios clientes de un mismo producto, aumentan los factores que afectan a la empresa, las herramientas disponibles no contemplan estos aspectos y hacen mucho más compleja la tarea de calificación. Este trabajo de investigación abarca la realización de un relevamiento de los métodos de priorización y selección de nuevos requerimientos utilizados por empresas de la zona de Rosario, y la definición de una metodología para la selección un nuevo requerimiento, que implica el análisis y evaluación de todas las implicaciones sobre el producto de software y la empresa, respetando sus reglas de negocio. La metodología creada conduce a la definición de los procesos para la construcción de una herramienta de calificación y priorización de nuevos requerimientos en software propietario que tiene solicitudes de varios clientes al mismo tiempo, con instrumentos de calificación que consideran todos los aspectos relacionados, proveerá técnicas de priorización actuales y emitirá informes personalizados según diferentes perspectivas de la empresa.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI

Morpho-syntactically annotated corpora provided for the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)

This multilingual resource contains corpora for 14 languages, gathered at the occasion of the 1.2 edition of the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). These corpora were meant to serve as additional "raw" corpora, to help discovering unseen verbal MWEs. The corpora are provided in CONLL-U (https://universaldependencies.org/format.html) format. They contain morphosyntactic annotations (parts of speech, lemmas, morphological features, and syntactic dependencies). Depending on the language, the information comes from treebanks (mostly Universal Dependencies v2.x) or from automatic parsers trained on UD v2.x treebanks (e.g., UDPipe). VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). For the 1.2 shared task edition, the data covers 14 languages, for which VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format. Morphological and syntactic information ­­­­– not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.2 (2020). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.

PARSEME corpora annotated for verbal multiword expressions (version 1.3)

This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). This is the first release of the corpora without an associated shared task. Previous version (1.2) was associated with the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). The data covers 26 languages corresponding to the combination of the corpora for all previous three editions (1.0, 1.1 and 1.2) of the corpora. VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format. Morphological and syntactic information, ­­­­including parts of speech, lemmas, morphological features and/or syntactic dependencies, are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). All corpora are split into training, development and test data, following the splitting strategy adopted for the PARSEME Shared Task 1.2. The annotation guidelines are available online: https://parsemefr.lis-lab.fr/parseme-st-guidelines/1.3 The .cupt format is detailed here: https://multiword.sourceforge.net/cupt-format