At ACL 2019, IBM researchers released a paper detailing the model they trained to answer complex questions using Neural Program Induction, which allows an AI model can be taught to procedurally decompose a complex task into a program.
There is a growing number of adversarial attacks and nefarious behaviors aimed at AI systems. To combat this, IBM Research AI will present multiple papers that yield new scientific discoveries and recommendations related to adversarial learning at KDD 2019.
IBM Research AI and the University of Michigan are organizing a public competition to inspire and evaluate novel approaches that will lead to the next generation of AI-driven dialog systems.
A team of researchers from IBM Research AI and AI Horizons Network-partner the University of Michigan published the papers “A Large-Scale Corpus for Conversation Disentanglement” and “Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use” at ACL 2019. This work address two main challenges in building enterprise AI assistants.
At ACL 2019, IBM researchers will present a demonstration of HEIDL, a model that makes it easier and much faster for people to review the effectiveness of natural language labels generated by a deep learning model trained on human-labeled data.
IBM Research AI and IBM Watson worked together to develop a promising approach that achievies state-of-the-art performance on relation extraction. This work is being presented at ACL 2019.
The IBM Science Summarizer, or DimSum, as it was nicknamed by the team for its DIscovery augMented SUMmarization, is a service that tracks scientific papers being published in the area of AI. The service produces summaries of the papers focused around an information need expressed through natural language queries.
The latest work on computational argumentation from the IBM Project Debater research team group is being presented at the ACL 2019 conference. Three papers will be presented at the main conference and one more paper will be presented in the co-located Argument Mining Workshop.