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Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the(More)
We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the(More)
We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth(More)
We present novel methods for analysing the activation patterns of RNNs and identifying the types of linguistic structure they learn. As a case study, we use a multi-task gated recurrent network model consisting of two parallel pathways with shared word embeddings trained on predicting the representations of the visual scene corresponding to an input(More)
Children learn the meaning of words by being exposed to perceptually rich situations (linguistic discourse, visual scenes, etc). Current computational learning models typically simulate these rich situations through impoverished symbolic approximations. In this work, we present a distributed word learning model that operates on child-directed speech paired(More)
We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data(More)
As societies move towards integration of robots, it is important to study how robots can use their cognition in order to choose effectively their actions in a human environment, and possibly adapt to new contexts. When modelling these contextual data, it is common in social robotics to work with data extracted from human sciences such as sociology, anatomy,(More)
Community Question Answering websites (CQA) offer a new opportunity for users to provide, search and share knowledge. Although the idea of receiving a direct, targeted response to a question sounds very attractive, the quality of the question itself can have an important effect on the likelihood of getting useful answers. High quality questions improve the(More)