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Full-Text Articles in Computational Linguistics
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Conference papers
In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher
Articles
Errors in visual perception may cause problems in situated dialogues. We investigated this problem through an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot.We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions.We then introduced different methods for the user to request information about the robot’s understanding of the environment. We quantify the impact of perception errors on the dialogues, and investigate resolution attempts by users at a structural level and at the level of referring expressions.