CUED Publications database

Paraphrastic neural network language models

Liu, X and Gales, MJF and Woodland, PC (2014) Paraphrastic neural network language models. In: UNSPECIFIED pp. 4903-4907..

Full text not available from this repository.


Expressive richness in natural languages presents a significant challenge for statistical language models (LM). As multiple word sequences can represent the same underlying meaning, only modelling the observed surface word sequence can lead to poor context coverage. To handle this issue, paraphrastic LMs were previously proposed to improve the generalization of back-off n-gram LMs. Paraphrastic neural network LMs (NNLM) are investigated in this paper. Using a paraphrastic multi-level feedforward NNLM modelling both word and phrase sequences, significant error rate reductions of 1.3% absolute (8% relative) and 0.9% absolute (5.5% relative) were obtained over the baseline n-gram and NNLM systems respectively on a state-of-the-art conversational telephone speech recognition system trained on 2000 hours of audio and 545 million words of texts. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:01
Last Modified: 19 Jul 2018 07:55