8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Minimum Rank Error Training for Language Modeling

Meng-Sung Wu, Jen-Tzung Chien

National Cheng Kung University, Taiwan

Discriminative training techniques have been successfully developed for many pattern recognition applications. In speech recognition, discriminative training aims to minimize the metric of word error rate. However, in an information retrieval system, the best performance should be achieved by maximizing the average precision. In this paper, we construct the discriminative n-gram language model for information retrieval following the metric of minimum rank error (MRE) rather than the conventional metric of minimum classification error. In the optimization procedure, we maximize the average precision and estimate the language model towards attaining the smallest ranking loss. In the experiments on ad-hoc retrieval using TREC collections, the proposed MRE language model performs better than the maximum likelihood and the minimum classification error language models.

Full Paper

Bibliographic reference.  Wu, Meng-Sung / Chien, Jen-Tzung (2007): "Minimum rank error training for language modeling", In INTERSPEECH-2007, 614-617.