On the Use of Neural Network Modeling Techniques for Spoken Document Retrieval

Due to ever-increasing amounts of publicly available multimedia associated with speech information, spoken document retrieval (SDR) has been an active area of research that captures significant interest from both academic and industrial communities. Beyond the continuing effort in the development of robust indexing and effective retrieval methods to quantify the relevance degree between a pair of query and spoken document, how to accurately and efficiently model the query content plays a vital role for improving SDR performance. In view of this, we present in this paper a novel neural relevance-aware model (NRM) to infer an enhanced query representation, extricating the conventional time-consuming pseudo-relevance feedback (PRF) process. In addition, we incorporate the notion of query intent classification into our proposed NRM modeling framework to obtain more sophisticated query representations. Preliminary experiments conducted on the TDT-2 collection confirm the utility of our methods in relation to a few state-of-the-art ones. Keyword : Spoken Document Retrieval, Query Intent, Neural Network, Pseudo-Relevance Feedback 1. 緒論 (INTRODUCTION) 伴隨著網際網路的發展與多媒體資訊的大量增長,影音的瀏覽與傳遞也逐漸成為我們的 日常生活的一部分。在這環境下,如何利用語音的資訊,快速檢索符合資訊需求的內容, 變成了一項新興的需求。因此,在過去的二十年(Chelba, Hazen & Saraclar, 2008) (Lee & Chen, 2005) (Huang, Ma, Li & Wu, 2011) (Chen, Chen, Chen & Chen, 2012),語音文件檢索 成為一個十分有魅力的研究主題。在語音文件檢索的任務上,過往有許多顯著成功的方 法,如向量空間模型(Vector Space Model) (Salton, Wong & Yang, 1975)、Okapi BM25 model (Jones, Walker & Robertson, 2000),以及主題模型(Topic Model) (Blei, Ng & Jordan, 2003)等。另一方面,將統計式語言模型(Statistical Language Model)應用在文字檢索 (Information Retrieval)和語音文件檢索,在檢索任務上取得了嶄新的突破(Ponte & Croft, 1998) (Song & Croft, 1999) (Croft & Lafferty, 2003),因此吸引了不少研究者的目光。在這 樣的概念下,查詢對每個文件計算似然機率後作排名,我們稱這樣的排序方法為查詢似 然測量(Query Likelihood Model Measure, QLM) (Manning, Raghavan & Schutze, 2008)。另 一個知名的評估方式為 KL 散度測量(Kullback-Leibler Divergence Measure, KLM) (Zhai & Lafferty, 2001),將查詢與文件皆表示為單元語法的語言模型(Unigram Language Model), 查詢與文件的相似程度即為兩個機率分佈的散度距離(Divergence Distance)。 最近,隨著深層類神經網路架構的流行,這類的方法也被大量應用在檢索的任務上。 主要的研究方向為利用不同網路架構與訓練方法,以此來學習查詢與文件間的相似關係 (Guo, Fan, Ai & Croft, 2016) (Mitra, Diaz & Craswell, 2017)。值得注意的是,大部分方法