Neural relevance-aware query modeling for spoken document retrieval

Spoken document retrieval (SDR) is becoming a much-needed application due to that unprecedented volumes of audio-visual media have been made available in our daily life. As far as we are aware, most of the wide variety of SDR methods mainly focus on exploring robust indexing and effective retrieval methods to quantify the relevance degree between a pair of query and document. However, similar to information retrieval (IR), a fundamental challenge facing SDR is that a query is usually too short to convey a user's information need, such that a retrieval system cannot always achieve prospective efficacy when with the existing retrieval methods. In order to further boost retrieval performance, several studies turn their attention to reformulating the original query by leveraging an online pseudo-relevance feedback (PRF) process, which often comes at the price of taking significant time. Motivated by these observations, this paper presents a novel extension of the general line of SDR research and its contribution is at least two-fold. First, building on neural network-based techniques, we put forward a neural relevance-aware query modeling (NRM) framework, which is designed to not only infer a discriminative query language model automatically for a given query, but also get around the time-consuming PRF process. Second, the utility of the methods instantiated from our proposed framework and several widely-used retrieval methods are extensively analyzed and compared on a standard SDR task, which suggests the superiority of our methods.