Amazon Machine Learning Research Awards generously sponsored my colleagues and me to participate in last week’s Amazon re:MARS Conference. It was a global artificial intelligence (AI) event focused on Machine Learning, Automation, Robotics, and Space.
The conference was great with accessibility. I was assigned an employee who guided me everywhere and was just wonderful. The conference website was accessible and easy to navigate. When I identified accessibility problems with the mobile app and with SageMaker tools, Amazon personnel immediately assured me they would be fixed.
The sponsorship included participation in the re:MARS VIP Leadership Networking Reception. I was honored to speak with members of Amazon leadership as well as senior researchers from industry and academia.
- my AI-driven, Web text simplification research;
- AI fairness for people with disabilities; and
- developing an Alexa skill for DisabilityInfo.org.
I will soon present part of my AI-Driven Web Text Simplification research.
We tested if people with intellectual disabilities understand Web text simplified with plain-language standards. (Spoiler: They do!)
We are operationalizing plain-language standards essentially to develop:
- a reliable, easy-to-use method for human editors to create simple text; and
- algorithms for AI to recognize and to create simple text.
For my AI-Driven Web Text Simplification research, I lead a coalition of corporate and academic partners. They include:
Make Web text so simple people understand it the first time they read it.
Text comprises the vast majority of Web content. Poor reading comprehension presents significant challenges to many populations, including people with cognitive disabilities, non‐native speakers, and people with low literacy.
Text simplification aims to reduce text complexity while retaining its meaning. Manual text simplification research has been ongoing for decades. Yet no significant effort has been made to automate text simplification except as a preprocessor for natural-language processing tasks such as machine translation and summarization.
In the short term, my partners and I are improving manual text simplification by creating effective, replicable methods for humans to produce it. We use national and international plain language standards. We conduct pilot studies to see if people comprehend our human-curated, simplified Web text better than typical Web text.
In the long term, my partners and I are developing artificial intelligence (AI) capabilities to produce simple Web text on a mass scale. We are training AI with enormous sets of aligned sentence pairs (typical/simple). We will soon start crowd-sourcing the generation of training data.
I will provide details in future blog posts.