We present a statistical model for predicting how the user of an interactive, situated NLP system resolved a referring expression. The model makes an initial prediction based on the meaning of the utterance, and revises it continuously based on the user behavior. The combined model outperforms its components in predicting reference resolution and when to give feedback.
|Title of host publication||Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL|
|Publisher||Association for Computational Linguistics|
|Number of pages||6|
|Publication status||Published - 2013|