Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible — e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. In this talk, I will present some first steps towards addressing these problems, discussing our research on exaggeration detection, scientific fact checking, and on modelling information change in scientific communication more broadly.

Beyond Fact Checking — Modelling Information Change in Scientific Communication

In this talk, I will share my perspective on making conversational AI more inclusive and accessible through advances in natural language understanding, speech recognition, and by incorporating interaction affordances that make conversational AI more useful for everyone. I will start with a brief overview of accessibility considerations and some Alexa-powered user experiences that address those considerations. That overview will be followed by a description of some of our recent and ongoing AI/ML work in natural language understanding (Alexa Teacher Model) and in ASR with a focus on achieving fairness at scale. I will conclude with a brief mention of our engagements with academia, including through the Alexa Prize and through the release of publicly available datasets and associated research tasks.

Frontiers of Fair and Accessible AI

Deep nets are great, and they would be even greater if they could collaborate with each other through a flexible communication protocol. Since many networks come equipped with language model interfaces--that is, they can process and produce tokens in English (or other languages)--it makes sense to look into natural language as a universal neural network communication protocol. The questions I would like to discuss in my talk, then, are the following:<br>
* What are the challenges we face in developing a universal language interface among deep networks?
* What do we currently know about the way in which deep nets are using language to interact in machine-to-machine communication scenarios?
* Which characteristics of human linguistic communication do we expect or wish machine-to-machine communication to possess? Should it be essentially the same as human-to-human (and human-to-machine) communication?

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