Seguimos comprometidos com a pesquisa com o intuito de aprimorar as tecnologias linguísticas

A Prompsit se orgulha de continuar participando ativamente em pesquisas
realizadas nas nossas principais áreas de especialização

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Mostra 51-60 de 64 publicações (página 6 de 7)

2014

Quality Estimation for Synthetic Parallel Data Generation.

Antonio Toral, Guillermo Latour, Stanislav Gurevich

+2 more

Mikel Forcada, Gema Ramírez-Sánchez

This paper presents a novel approach for parallel data generation using machine translation and quality estimation. Our study focuses on pivot-based machine translation from English to Croatian throug...

h Slovene. We generate an English–Croatian version of the Europarl parallel corpus based on the English–Slovene Europarl corpus and the Apertium rule-based translation system for Slovene–Croatian. These experiments are to be considered as a first step towards the generation of reliable synthetic parallel data for under-resourced languages. We first collect small amounts of aligned parallel data for the Slovene–Croatian language pair in order to build a quality estimation system for sentence-level Translation Edit Rate (TER) estimation. We then infer TER scores on automatically translated Slovene to Croatian sentences and use the best translations to build an English–Croatian statistical MT system. We show significant improvement in terms of automatic metrics obtained on two test sets using our approach compared to a random selection of synthetic parallel data.

2013

Incorporating Subject Areas into the Apertium Machine Translation System

Jordi Duran, Lluís Villarejo, Mireia Farrús

+2 more

Sergio Ortiz, Gema Ramírez

The Universitat Oberta de Catalunya (Open University of Catalonia, UOC), is a public university based in Barcelona. The UOC is characterised by three main factors: (a) it is a virtual university based...

in an e-Learning model, (b) it is based in a strongly Spanish-Catalan bilingual region, and (c) students come from around the world, so that linguistic and cultural diversity is a crucial factor. Within this context, it becomes essential to meet the UOC’s linguistic needs taking into account its particular characteristics. One of the tools created to this end is the adaptation of Apertium, a free/open-source rule-based machine translation platform, which can be found under http://apertium.uoc.edu/, customised to the translation needs of the institution in order to offer the best possible service to their user community.

2009

Desarrollo de un sistema libre de traducción automática del euskera al castellano

Mireia Ginestí-Rosell, Gema Ramírez-Sánchez, Sergio Ortiz-Rojas

+2 more

Francis M Tyers, Mikel L Forcada

Este artículo presenta un sistema de traducción automática libre (de código abierto) basado en reglas entre euskera y castellano, construido sobre la plataforma de traducción automática Apertium y pen...

sado para la asimilación, es decir, como ayuda a la comprensión de textos escritos en euskera. Se describe el desarrollo y la situación actual y se muestra una evaluación de la calidad de las traducciones.

2015

EAMT 2015

İIknur Durgar El-Kahlout, Mehmed Özkan, Felipe Sánchez-Martínez

+3 more

Gema Ramírez-Sánchez, Fred Hollowood, Andy Way

This paper presents the work done to port a deep-transfer rule-based machine translation system to translate from a different source language by maximizing the exploitation of existing resources and b...

y limiting the development work. Specifically, we report the changes and effort required in each of the system’s modules to obtain an English-Basque translator, ENEUS, starting from the Spanish-Basque Matxin system. We run a human pairwise comparison for the new prototype and two statistical systems and see that ENEUS is preferred in over 30% of the test sentences.

2014

Abu-matran at wmt 2014 translation task: Two-step data selection and rbmt-style synthetic rules

Raphael Rubino, Antonio Toral, Victor M Sánchez-Cartagena

+5 more

Jorge Ferrández-Tordera, Sergio Ortiz-Rojas, Gema Ramírez‐Sánchez, Felipe Sánchez‐Martínez, Andy Way

This paper presents the machine translation systems submitted by the Abu-MaTran project to the WMT 2014 translation task. The language pair concerned is English–French with a focus on French as the ta...

rget language. The French to English translation direction is also considered, based on the word alignment computed in the other direction. Large language and translation models are built using all the datasets provided by the shared task organisers, as well as the monolingual data from LDC. To build the translation models, we apply a two-step data selection method based on bilingual crossentropy difference and vocabulary saturation, considering each parallel corpus individually. Synthetic translation rules are extracted from the development sets and used to train another translation model. We then interpolate the translation models, minimising the perplexity on the development sets, to obtain our final SMT system. Our submission for the English to French translation task was ranked second amongst nine teams and a total of twenty submissions.

2006

Evaluation of alignment methods for HTML parallel text

Enrique Sánchez-Villamil, Susana Santos-Antón, Sergio Ortiz-Rojas

+1 more

Mikel L Forcada

The Internet constitutes a potential huge store of parallel text that may be collected to be exploited by many applications such as multilingual information retrieval, machine translation, etc. These ...

applications usually require at least sentence-aligned bilingual text. This paper presents new aligners designed for improving the performance of classical sentence-level aligners while aligning structured text such as HTML. The new aligners are compared with other well-known geometric aligners.

2016

Producing monolingual and parallel web corpora at the same time-spiderling and bitextor’s love affair

Nikola Ljubešić, Miquel Esplà-Gomis, Antonio Toral

+2 more

Sergio Ortiz-Rojas, Filip Klubička

This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering l...

inguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain “. hr” and the Slovene top-level domain “. si”, and extrinsically on the English-Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English-Croatian, English-Finnish, English-Serbian and English-Slovene language pairs.

2017

MTradumàtica: Free Statistical Machine Translation Customisation for Translators

Gökhan Doğru, Adrià Martín-Mor, Sergio Ortiz-Rojas

MTradumàtica is a free, Moses-based web platform for training and using statistical machine translation systems with a user-friendly graphical interface. Its goal is to offer translators a free tool t...

o customise their own statistical machine translation engines and enhance their productivity. In this paper, we aim to describe the features of MTradumàtica and its advantages for translators by focusing on its current capabilities and limitations from a user perspective.

2009

Joint efforts to further develop and incorporate Apertium into the document management flow at Universitat Oberta de Catalunya

Luis Villarejo Munoz, Sergio Ortíz-Rojas, Mireia Ginestí-Rosell

This article describes the needs of UOC regarding translation and how these needs are satisfied by Prompsit further developing a free rule-based machine translation system: Apertium. We initially desc...

ribe the general framework regarding linguistic needs inside UOC. Then, section 2 introduces Apertium and outlines the development scenario that Prompsit executed. After that, section 3 outlines the specific needs of UOC and why Apertium was chosen as the machine translation engine. Then, section 4 describes some of the features specially developed in this project. Section 5 explains how the linguistic data was improved to increase the quality of the output in Catalan and Spanish. And, finally, we draw conclusions and outline further work originating from the project.

2016

Cloudlm: a cloud-based language model for machine translation

Jorge Ferrández-Tordera, Sergio Ortiz-Rojas, Antonio Toral

Language models (LMs) are an essential element in statistical approaches to natural language processing for tasks such as speech recognition and machine translation (MT). The advent of big data leads ...

to the availability of massive amounts of data to build LMs, and in fact, for the most prominent languages, using current techniques and hardware, it is not feasible to train LMs with all the data available nowadays. At the same time, it has been shown that the more data is used for a LM the better the performance, eg for MT, without any indication yet of reaching a plateau. This paper presents CloudLM, an open-source cloud-based LM intended for MT, which allows to query distributed LMs. CloudLM relies on Apache Solr and provides the functionality of state-of-the-art language modelling (it builds upon KenLM), while allowing to query massive LMs (as the use of local memory is drastically reduced), at the expense of slower decoding speed.