Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education
Table 2. Experimental results on phonological relevance detection.
['[EMPTY]', '[BOLD] LR', '[BOLD] SiameseNet', '[BOLD] TripleNet', '[BOLD] RelationNet', '[BOLD] Our']
We choose logistic regression as the basic classifier. We conduct pair wise t-tests between our approach and every baseline mentioned above and the results show that the prediction outcomes are statistically significant at 0.05 level. This is due to the fact that our discriminative embedding learning directly optimizes the distance between positive and negative examples. By generating all the groups, we want to separate each positive pair with a few negative examples as far as possible. In terms of TripleNet and RelationNet, both of them optimize the distance function between positive and negative indirectly. For example, TripletNet generates more training samples by creating triplet, which contains anchor, positive and negative examples. It fully utilizes the anchor to connect the positive and negative examples during the training. However, selecting triplet is not straightforward. SiameseNet has the worst performance because it is designed for one-shot learning, which aims to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. This may lead to the inferior performance in our situation that both positive and negative examples are very limited.