  An important task for the design of Question Answering systems is the
selection of the sentence containing (or constituting) the answer from
documents relevant to the asked question. Most previous work has only used the
target sentence to compute its score with the question as the models were not
powerful enough to also effectively encode additional contextual information.
In this paper, we analyze the role of the contextual information in the
sentence selection task, proposing a Transformer based architecture that
leverages two types of contexts, local and global. The former describes the
paragraph containing the sentence, aiming at solving implicit references,
whereas the latter describes the entire document containing the candidate
sentence, providing content-based information. The results on three different
benchmarks show that the combination of local and global contexts in a
Transformer model significantly improves the accuracy in Answer Sentence
Selection.
