Name
Abstract | ||
|---|---|---|
The richness of multimodal dialogue makes the space of possible features required to describe it very large relative to the amount of training data. However, conventional classifier learners require large amounts of data to avoid overfitting, or do not generalize well to unseen examples. To learn dialogue classifiers using a rich feature set and fewer data points than features, we apply a recent technique, ℓ1-regularized logistic regression. We demonstrate this approach empirically on real data from Project LISTEN's Reading Tutor, which displays a story on a computer screen and listens to a child read aloud. We train a classifier to predict task completion (i.e., whether the student will finish reading the story) with 71% accuracy on a balanced, unseen test set. To characterize differences in the behavior of children when they choose the story they read, we likewise train and test a classifier that with 73.6% accuracy infers who chose the story based on the ensuing dialogue. Both classifiers significantly outperform baselines and reveal relevant features of the dialogue. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1966407.1966413 | TSLP |
Keywords | Field | DocType |
training data,accuracy infers,high-dimensional space,unseen test set,conventional classifier learner,unseen example,multimodal dialogue,rich feature set,large amount,fewer data point,classifying dialogue,feature engineering,ell,feature selection,logistic regression | Feature selection,Computer science,Feature engineering,Artificial intelligence,Natural language processing,Overfitting,Classifier (linguistics),Task completion,Data point,TUTOR,Speech recognition,Machine learning,Test set | Journal |
Volume | Issue | ISSN |
7 | 3 | 1550-4875 |
Citations | PageRank | References |
3 | 0.41 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José P. González-Brenes | 1 | 91 | 9.77 |
| Jack Mostow | 2 | 1133 | 263.51 |