  The usage of neural network models puts multiple objectives in conflict with
each other: Ideally we would like to create a neural model that is effective,
efficient, and interpretable at the same time. However, in most instances we
have to choose which property is most important to us. We used the opportunity
of the TREC 2019 Deep Learning track to evaluate the effectiveness of a
balanced neural re-ranking approach. We submitted results of the TK
(Transformer-Kernel) model: a neural re-ranking model for ad-hoc search using
an efficient contextualization mechanism. TK employs a very small number of
lightweight Transformer layers to contextualize query and document word
embeddings. To score individual term interactions, we use a document-length
enhanced kernel-pooling, which enables users to gain insight into the model.
Our best result for the passage ranking task is: 0.420 MAP, 0.671 nDCG, 0.598
P@10 (TUW19-p3 full). Our best result for the document ranking task is: 0.271
MAP, 0.465 nDCG, 0.730 P@10 (TUW19-d3 re-ranking).
