Paper: Learning To Predict Pitch Accents And Prosodic Boundaries In Dutch

ACL ID P03-1062
Title Learning To Predict Pitch Accents And Prosodic Boundaries In Dutch
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shal- low, easy-to-compute features. We train the algorithms on both tasks individu- ally and on the two tasks simultaneously. The parameters of both algorithms and the selection of features are optimized per task with iterative deepening, an efficient wrapper procedure that uses progressive sampling of training data. Results show a consistent significant advantage of MBL over CART, and also indicate that task combination can be done at the cost of little generalization score loss. Tests on cross-validated data and on held-out data yield F-scores of MBL on accent place- ment of 84 and 87, respectively, and on breaks of 88 and 91, respect...