Reference Resolution in Situated Dialogue with Learned Semantics
Xiaolong Li, Kristy Elizabeth Boyer · 2016
Understanding situated dialogue requires identifying referents in the environment to which the dialogue participants refer.This reference resolution problem, often in a complex environment with high ambiguity, is very challenging.We propose an approach that addresses those challenges by combining learned semantic structure of referring expressions with dialogue history into a ranking-based model.We evaluate the new technique on a corpus of human-human tutorial dialogues for computer programming.The experimental results show a substantial performance improvement over two recent state-of-the-art approaches.The proposed work makes a stride toward automated dialogue in complex problem-solving environments.