e-SNLI: Natural Language Inference with Natural Language Explanations

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.Comment: NeurIPS 201

Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Social capital and political participation: understanding the dynamics of young people's political disengagement in contemporary Britain

Only 37 per cent of young people voted at the 2005 British General Election, seemingly confirming the oft-cited view that this generation is becoming increasingly disconnected from the political process. Results from a nationwide survey however, indicate that their withdrawal from formal politics is more a result of their scepticism of the way the political system operates, than apathy. Furthermore, they are diverse in their political (dis)engagement. Results from an examination of the relative effects of socio-economic location and social capital are inconclusive, although the data indicate that government social policy aimed at mobilising social capital and addressing socio-economic issues may increase civic engagement