We have also investigated effective machine learning techniques
for identifying dialogue acts. Although Transformation-Based
Learning (TBL) has a number of attractive features, it has not previously been
applied to discourse level problems. This work, pursued with Ken Samuel
and K. Vijay-Shanker, investigates modifications and enhancements of
TBL and application of TBL to the problem of recognizing dialogue acts.
One limitation of TBL is that the algorithm quickly becomes intractable
if the number of potential rules under consideration is not severely
limited. This research produced a Monte Carlo version of
TBL that overcomes this limitation by improving training time
efficiency without significant degradation in performance on unseen data.
It also provided other enhancements such as a committee method that
enables TBL to associate confidence measures with the assigned dialogue
act tags. Other contributions of the research include an entropy
minimization approach to identifying useful dialogue act cues.
Although additional improvements remain to be investigated,
the modified TBL algorithm has already achieved a success rate
equivalent to the best
reported results on the dialogue act tagging problem.
Stephanie Elzer, Sandra Carberry, Ingrid Zukerman, Daniel Chester, Nancy Green, and Seniz Demir. A Probabilistic Framework for Recognizing Intention in Information Graphics. Proceedings of the Nineteenth International Conference on Artificial Intelligence (IJCAI-05), 2005. pdf version
Sandra Carberry, Ken Samuel, K. Vijay-Shanker, and Andrew Wilson. Randomized Rule Selection in Transformation-Based Learning: A Comparative Study. Natural Language Engineering, 7(2), pp. 99-116, 2001. postscript version
Samuel, Ken and Sandra Carberry and K. Vijayashanker. Automatically Selecting Useful Phraes for Dialogue Act Tagging. Proceeding of the Meeting of the Pacific Association for Computational Linguistics, 1999.
Samuel, Ken and Sandra Carberry and K. Vijay-Shanker) An Investigation of Transformation-Based Learning in Discourse. Proceedings of the International Conference on Machine Learning (ICML) , pp. 497-505, 1998.
Samuel, Ken, Sandra Carberry, and K. Vijay-Shanker. Dialogue Act Tagging with Transformation-Based Learning. Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1150-1156, 1998.
Samuel, Ken and Sandra Carberry and K. Vijay-Shanker. Computing Dialogue Acts from Features with Transformation-Based Learning. Proceedings of the AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 90-97, 1998.