Eva Martínez Garcia

Weighted Set-Theoretic Alignment of Comparable Sentences.

This article presents the STACCw system for the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. The original STACC approach, based on set-theoretic operations over bags of words, had been previously shown to be efficient and portable across domains and alignment scenarios. We describe an extension of this approach with a new weighting scheme and show that it provides significant improvements on the datasets provided for the shared task.

Word's Vector Representations meet Machine Translation

Distributed vector representations of words are useful in various NLP tasks. We briefly review the CBOW approach and propose a bilingual application of this architecture with the aim to improve consistency and coherence of Machine Translation. The primary goal of the bilingual extension is to handle ambiguous words for which the different senses are conflated in the monolingual setup.

QUALES: Estimación Automática de Calidad de Traducción Mediante Aprendizaje Automático Supervisado y No-Supervisado

La estimacion automatica de calidad (EAC) de la traduccion automatica consiste en medir la calidad de traducciones sin acceso a referencias humanas, habitualmente mediante metodos de aprendizaje automatico. Un buen sistema EAC puede ayudar en tres aspectos del proceso de traduccion asistida por medio de traduccion automatica y posedicion: aumento de la productividad (descartando traducciones automaticas de mala calidad), estimacion de costes (ayudando a prever el coste de posedicion) y seleccion de proveedor (si se dispone de varios sistemas de traduccion automatica). El interes en este campo de investigacion ha crecido significativamente en los ultimos anos, dando lugar a tareas compartidas a nivel mundial (WMT) y a una fuerte actividad cientifica. En este articulo, se hace un repaso del estado del arte en este area y se presenta el proyecto quales que se esta realizando.

Using Word Embeddings to Enforce Document-Level Lexical Consistency in Machine Translation

Abstract We integrate new mechanisms in a document-level machine translation decoder to improve the lexical consistency of document translations. First, we develop a document-level feature designed to score the lexical consistency of a translation. This feature, which applies to words that have been translated into different forms within the document, uses word embeddings to measure the adequacy of each word translation given its context. Second, we extend the decoder with a new stochastic mechanism that, at translation time, allows to introduce changes in the translation oriented to improve its lexical consistency. We evaluate our system on English–Spanish document translation, and we conduct automatic and manual assessments of its quality. The automatic evaluation metrics, applied mainly at sentence level, do not reflect significant variations. On the contrary, the manual evaluation shows that the system dealing with lexical consistency is preferred over both a standard sentence-level and a standard document-level phrase-based MT systems.