Automatic Response-to-Text Assessment in the Evidence Dimension with Word Embedding and Topic Importance Model

Type Coffee Hour Talk
Start Date March 13, 2017 03:00 PM
End Date March 13, 2017 04:00 PM
Location 5317 Sennott Square
Organizer Name
Speaker Name Haoran Zhang
Abstract This work investigates the score prediction for evidence dimension of Response-to-Text Assessment (RTA). In previous work of this project, a new set of interpretable features has been designed for evaluating evidence dimension of RTA. The results show that the new set of features outperform baselines. In this work, we are trying to improve the performance of the previous model by introducing word embedding and topic importance models into the features extraction process. We will talk about the preliminary results of this work.