Leveraging effective query modeling techniques for speech recognition and summarization

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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.

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Chen, K. Y., Liu, S. H., Chen, B., Jan, E. E., Wang, H. M., Hsu, W. L., & Chen, H. H. (2014). Leveraging effective query modeling techniques for speech recognition and summarization. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 1474–1480). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/d14-1156

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