Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization

Abstract

Statistical language modeling (LM) that purports to quantify the acceptability of a given piece of text has long been an interesting yet challenging research area. In particular, language modeling for information retrieval (IR) has enjoyed remarkable empirical success; one emerging stream of the LM approach for IR is to employ the pseudo-relevance feedback process to enhance the representation of an input query so as to improve retrieval effectiveness. This paper presents a continuation of such a general line of research and the main contribution is threefold. First, we propose a principled framework which can unify the relationships among several widely-used query modeling formulations. Second, on top of the successfully developed framework, we propose an extended query modeling formulation by incorporating critical query-specific information cues to guide the model estimation. Third, we further adopt and formalize such a framework to the speech recognition and summarization tasks. A series of empirical experiments reveal the feasibility of such an LM framework and the performance merits of the deduced models on these two tasks.

Generalized Analysis of a Distribution Separation Method

“…Although Chen et al have summarized RM and MMF in the mixture multinomial distribution [12], they have not shown that both RM and MMF comply with the linear combination assumption. With the proof in Appendix A.3, we demonstrate Proposition 5, which shows that MMF complies with the linear combination assumption.…”

“…In this section, we will compare DSM with other related works, including mixture model feedback (MMF) [7], fast mixture model feedback (FMMF) [10], regularized mixture model feedback (RMMF) [11], as well as a mixture multinomial distribution framework and a query-specific mixture modelling feedback (QMMF) approach [12]. Since the above models are implemented on two basic relevance feedback models, i.e., relevance model (RM) and mixture model feedback (MMF), we will also compare RM (we use RM to denote RM1 in [2]) and MMF.…”

“…Chen et al [12] proposed a unified framework by considering several query expansion models, e.g., RM and MMF, as mixture multinomial distributions. In addition, they built a query-specific mixture model feedback (QMMF) approach, which modifies RMMF by replacing the original query model with the relevance model (actually RM1) in the prior function of RMMF.…”

“…In addition, we can replace the p(z w = 1|F , θ (n) F ) in Equation (12) using Equation (11) and then get: We can obtain Equation (30) for any word w * in the vocabulary, and now, we sum them together as follows: …”