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Found 11 papers, 1 papers with code
Towards a World-English Language Model for On-Device Virtual Assistants
Based on this insight and leveraging the design of our production models, we introduce a new architecture for World English NNLM that meets the accuracy, latency, and memory constraints of our single-dialect models.
Neural Language Model Pruning for Automatic Speech Recognition
We study model pruning methods applied to Transformer-based neural network language models for automatic speech recognition.
Application-Agnostic Language Modeling for On-Device ASR
On-device automatic speech recognition systems face several challenges compared to server-based systems.
Space-Efficient Representation of Entity-centric Query Language Models
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Connecting and Comparing Language Model Interpolation Techniques
In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation.
A Neural Network Approach for Mixing Language Models
The performance of Neural Network (NN)-based language models is steadily improving due to the emergence of new architectures, which are able to learn different natural language characteristics.
Long-Short Range Context Neural Networks for Language Modeling
The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora.
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
Training large vocabulary Neural Network Language Models (NNLMs) is a difficult task due to the explicit requirement of the output layer normalization, which typically involves the evaluation of the full softmax function over the complete vocabulary.
Sequential Recurrent Neural Networks for Language Modeling
Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network.
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling
We augmented pre-trained word embeddings with these novel embeddings and evaluated on a rare word similarity task, obtaining up to 3 times improvement in correlation over the original set of embeddings.
