Phrase-based machine translation assumes that all words are at the same distance and translates them using feature functions that approximate the probability at different levels. On the other hand, neural machine translation infers a word embedding and translates these word vectors using a neural model. At the moment, both approaches co-exist and are being intensively investigated. This paper to the best of our knowledge is the first work that both compares and combines these two systems by: using the phrase-based output to solve unknown words in the neural machine translation output; using the neural alignment in the phrase-based system; comparing how the popular strategy of pre-reordering aspects both systems; and combining both translation outputs. Improvements are achieved in Catalan-to- Spanish and German-to-English.
This paper describes an Information Retrieval engine that is used to
support our Chinese-Portuguese machine translation services when no internet
connection is available. Our mobile translation app, which is deployed on a
portable device, relies by default on a server-based machine translation service,
which is not accessible when no internet connection is available. For providing
translation support under this condition, we have developed a contextualized
off-line search engine that allows the users to continue using the app.
This introductory paper serves as an overview about the rest of the
papers that are contained within this volume. In this article it is presented our vision of the Citizen Sensor Networks as twofold: one where the citizens are passive entities that need to be tracked to
understand and optimize better the SmartCities, and the second where the citizens, motivated by their common sense and using their mobile device to communicate the sensed sample. Also in this abstract we will introduce the concept of crowd sourcing or crowd
compution and its industrial applications.