4 research outputs found
#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
Inspired by the recent social movement of #MeToo, we are building a chatbot
to assist survivors of sexual harassment cases (designed for the city of
Maastricht but can easily be extended). The motivation behind this work is
twofold: properly assist survivors of such events by directing them to
appropriate institutions that can offer them help and increase the incident
documentation so as to gather more data about harassment cases which are
currently under reported. We break down the problem into three data
science/machine learning components: harassment type identification (treated as
a classification problem), spatio-temporal information extraction (treated as
Named Entity Recognition problem) and dialogue with the users (treated as a
slot-filling based chatbot). We are able to achieve a success rate of more than
98% for the identification of a harassment-or-not case and around 80% for the
specific type harassment identification. Locations and dates are identified
with more than 90% accuracy and time occurrences prove more challenging with
almost 80%. Finally, initial validation of the chatbot shows great potential
for the further development and deployment of such a beneficial for the whole
society tool.Comment: 19 pages, accepted at SoGood2019 workshop (ECMLPKDD2019
Do Bayesian Variational Autoencoders Know What They Don't Know?
The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for OoDs, assigning to them a higher density than to the in-distribution data. This over-confidence in a single model can be potentially mitigated with Bayesian inference over the model parameters that take into account epistemic uncertainty. This paper investigates three approaches to Bayesian inference: stochastic gradient Markov chain Monte Carlo, Bayes by Backpropagation, and Stochastic Weight Averaging-Gaussian. The inference is implemented over the weights of the deep neural networks that parameterize the likelihood of the Variational Autoencoder. We empirically evaluate the approaches against several benchmarks that are often used for OoD detection: estimation of the marginal likelihood utilizing sampled model ensemble, typicality test, disagreement score, and Watanabe-Akaike Information Criterion. Finally, we introduce two simple scores that demonstrate the state-of-the-art performance.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit