|Xiaolong Li and Kristy Boyer|
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.