Continuous space discriminative language modeling

Discriminative language modeling is a structured classification problem. Log-linear models have been previously used to address this problem. In this paper, the standard dot-product feature representation used in log-linear models is replaced by a non-linear function parameterized by a neural network. Embeddings are learned for each word and features are extracted automatically through the use of convolutional layers. Experimental results show that as a stand-alone model the continuous space model yields significantly lower word error rate (1% absolute), while having a much more compact parameterization (60%-90% smaller). If the baseline scores are combined, our approach performs equally well.