  Recent developments in deep learning have led to a significant innovation in
various classic and practical subjects, including speech recognition, computer
vision, question answering, information retrieval and so on. In the context of
natural language processing (NLP), language representations have shown giant
successes in many downstream tasks, so the school of studies have become a
major stream of research recently. Because the immenseness of multimedia data
along with speech have spread around the world in our daily life, spoken
document retrieval (SDR) has become an important research subject in the past
decades. Targeting on enhancing the SDR performance, the paper concentrates on
proposing a neural retrieval framework, which assembles the merits of using
language modeling (LM) mechanism in SDR and leveraging the abstractive
information learned by the language representation models. Consequently, to our
knowledge, this is a pioneer study on supervised training of a neural LM-based
SDR framework, especially combined with the pretrained language representation
methods.
