  This paper summarizes our participation in the SMART Task of the ISWC 2020
Challenge. A particular question we are interested in answering is how well
neural methods, and specifically transformer models, such as BERT, perform on
the answer type prediction task compared to traditional approaches. Our main
finding is that coarse-grained answer types can be identified effectively with
standard text classification methods, with over 95% accuracy, and BERT can
bring only marginal improvements. For fine-grained type detection, on the other
hand, BERT clearly outperforms previous retrieval-based approaches.
