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Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...
For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...
This article proposes a novel feature-extraction framework for inferring impression personality traits, emergent leadership skills, communicative competence, and hiring decisions. The proposed framework extracts multimodal features, describing each partici ...
The study of newspaper layout evolution through historical corpora has been addressed by diverse qualitative and quantitative methods in the past few years. The recent availability of large corpora of newspapers is now making the quantitative analysis of l ...
Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to ...
Speech-to-speech translation is a framework which recognises speech in an input language, translates it to a target language and synthesises speech in this target language. In such a system, variations in the speech signal which are inherent to natural hum ...
The recent availability of large corpora of digitized texts over several centuries opens the way to new forms of studies on the evolution of languages. In this thesis, we study a corpus of 4 million press articles covering a period of 200 years. The thesis ...
Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but also across different languages. Current unsupervised adversarial approaches ...
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on ...
Today's research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is costly, existing datasets contain only hundreds of samples at most, resulting in ...