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Static interpolation of exponential n-gram models using features of features
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
The best language model performance for a task is often achieved by interpolating language models built separately on corpora from multiple sources. While common practice is to use a single set of fixed interpolation weights to combine models, past work has found that gains can be had by allowing weights to vary by n-gram, when linearly interpolating word n-gram models. In this work, we investigate whether similar ideas can be used to improve log-linear interpolation for Model M, an exponential
doi:10.1109/icassp.2014.6854529
dblp:conf/icassp/SethyCRV14
fatcat:ruwktt5i6nf5nb6555jkg3dvei