One possible reason is that the FDLDA model is not specialized for supervised word clustering, and it uses a logistic regression model to predict the labels from the word topics, while the final soft word clustering is computed from the learned distribution p(z) and p(w|z).
for each different s value, we repeated the experiments 10 times by randomly selecting s labeled documents from the target domain using the same experimental setting as before.