  We implement a method for re-ranking top-10 results of a state-of-the-art
question answering (QA) system. The goal of our re-ranking approach is to
improve the answer selection given the user question and the top-10 candidates.
We focus on improving deployed QA systems that do not allow re-training or
re-training comes at a high cost. Our re-ranking approach learns a similarity
function using n-gram based features using the query, the answer and the
initial system confidence as input. Our contributions are: (1) we generate a QA
training corpus starting from 877 answers from the customer care domain of
T-Mobile Austria, (2) we implement a state-of-the-art QA pipeline using neural
sentence embeddings that encode queries in the same space than the answer
index, and (3) we evaluate the QA pipeline and our re-ranking approach using a
separately provided test set. The test set can be considered to be available
after deployment of the system, e.g., based on feedback of users. Our results
show that the system performance, in terms of top-n accuracy and the mean
reciprocal rank, benefits from re-ranking using gradient boosted regression
trees. On average, the mean reciprocal rank improves by 9.15%.
