  The emergence of structured databases for Question Answering (QA) systems has
led to developing methods, in which the problem of learning the correct answer
efficiently is based on a linking task between the constituents of the question
and the corresponding entries in the database. As a result, parsing the
questions in order to determine their main elements, which are required for
answer retrieval, becomes crucial. However, most datasets for QA systems lack
gold annotations for parsing, i.e., labels are only available in the form of
(question, formal-query, answer). In this paper, we propose a distantly
supervised learning framework based on reinforcement learning to learn the
mentions of entities and relations in questions. We leverage the provided
formal queries to characterize delayed rewards for optimizing a policy gradient
objective for the parsing model. An empirical evaluation of our approach shows
a significant improvement in the performance of entity and relation linking
compared to the state of the art. We also demonstrate that a more accurate
parsing component enhances the overall performance of QA systems.
