Paraphrastic recurrent neural network language models

Abstract

Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems. Linguistic factors in??uencing the realization of surface word sequences, for example, expressive richness, are only implicitly learned by RNNLMs. Observed sentences and their associated alternative paraphrases representing the same meaning are not explicitly related during training. In order to improve context coverage and generalization, paraphrastic RNNLMs are investigated in this paper. Multiple paraphrase variants were automatically generated and used in paraphrastic RNNLM training. Using a paraphrastic multi-level RNNLM modelling both word and phrase sequences, signi??cant error rate reductions of 0.6% absolute and perplexity reduction of 10% relative were obtained over the baseline RNNLM on a large vocabulary conversational telephone speech recognition system trained on 2000 hours of audio and 545 million words of texts. The overall improvement over the baseline n-gram LM was increased from 8.4% to 11.6% relative.