This paper describes the design, development, and analysis of a MOOC entitled “Approaches to Machine Translation: Rule-based, statistical and hybrid”, and provides lessons learned and conclusions to be taken into account ...

In this work, we explore the evolution of Sequential Neural Models, and their use as a Summarizer System. Transformer is a recently proposed model with a high potential. We experiment and compare their result in abstractive ...

This work reviews recent publications addressing the Twitter translation task, and highlights the lack of appropriate corpora that represents the colloquial language used in Twitter. It also discusses the most well-know ...

This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically ...

Short texts are typically composed of small number of words, most of which are abbreviations, typos and other kinds of noise. This makes the noise to signal ratio relatively high for this specific category of text. A high ...

Esta tesis explora diferentes maneras de implementar un sistema de bloques de Traducción de Voz con el propósito de generar grandes cantidades de datos para generar un gran corpus paralelo de voz. La primera tarea consiste ...

This article innovatively addresses machine translation from Chinese to Catalan using neural pivot strategies trained without any direct parallel data. The Catalan language is very similar to Spanish from a linguistic point ...

In Neural Machine Translation, using word-level tokens leads to degradation in translation quality. The dominant approaches use subword-level tokens, but this increases the length of the sequences and makes it difficult ...

This project has studied an unusual topic on image recognition and classification that can have numerous utilities on multimedia content. The approach studied on this project has been ...