This page is about my work as Research Assistant in Knowledge Media Institute at the Open University, UK.
Co-Inform (November 2018 - Current)
From November 2018 I have been working on Co-Inform, a EU-funded project. The objective is to create tools to foster critical thinking and digital literacy for a better-informed society. These tools will be designed and tested with policymakers, journalists, and citizens in 3 different EU countries.
The role of KMi in the project is to detect misinformation online and analyse how it spreads.
My first steps in the project have been to work on a demo that aims at seeing how much of known misinforming content is spreaded over the network of your friends.
The idea is, based on Twitter accounts, to have a platform where you can analyse yourself and your contacts to see from where you might be affected by misinformation. This corresponds to identifying profiles that share most misinforming content ans also identify where this content comes from.
So for the data collection, besides retrieving data about tweets and users from the Twitter API, there is a whole work of dataset collection and aggregation in order to have labeled data. The main sources of data are:
- fact-checking agencies: they all work on debunking and verifying stories that arise
- public datasets: they may provide labels for entire domains, for single URLs or even for claim-level statements
The platform analyses the URLs shared by the submitted profiles and and provides pointers to the fact-checking articles to the user, explaining in this way how the computed score came out.
NLU (May 2018 - November 2018)
The topics covered in this period are a continuation of the work done for the Master’s Thesis.
The main goal is to enable a voice interface on the robots in order to be able to receive commands and perform some actions.
The main challenges are related to:
- find and use a relevant dataset to train the NLU module
- perform the grounding of the objects mentioned
- analyze the feasibility of the actions required with respect to the abilities of the considered robot
- start the actions or suggest a simpler one in case the robot cannot perform the desired one
To tackle them, I have been experimenting with Neural Networks, Formal Concept Analysis, Knowledge Bases.
We have been dealing with different datasets (HuRIC, FrameNet) and analyzing how a Deep Learning model can be interpreted (in terms of looking where the attention of the neural network is captured).
During this analysis we found that, using small datasets, the neural network is attending at some unexpected words: we would expect to have the highest values on the verb (that for verbal commands are the Lexical Units of the Frame Semantics Theory) and on some discriminative prepositions. Adding more examples coming from FrameNet we are observing an alignment between the theory and the observed values.
Explainability and trust
This study underlines how important it is to have explainable models. After seeing related work in explainability, we realised that the analysis of the self-attention weights could be used as our definition of “explainable model”. When the values align with the linguistic theory, the model is behaving more similarly to how we would expect. In this case we define the explainability as the ability of the model to show up what words had the biggest contribution to provide the outputs.
On one side, an explainable model (in our case when the attention values align with the linguistic theory) brings more generalisability, that results in better performances with new samples.
On the other side, it leads to positive effects on the users, such as trust that may grow as an effect of knowing how the model is deciding internally (see the presentation at HAI 2018).