Training neural network language models over large vocabularies is still computationally
very costly compared to count-based models such as Kneser-Ney. At the same time, neural …

Existing language models such as n-grams for software code often fail to capture a long
context where dependent code elements scatter far apart. In this paper, we propose a novel …

Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more
complex computational unit, have been successfully applied to a variety of sequence …

Recurrent neural network language models (RNNLMs) have recently become increasingly
popular for many applications including speech recognition. In previous research RNNLMs …

In recent years, recurrent neural network language models (RNNLMs) have become
increasingly popular for a range of applications including speech recognition. However, the …

End-to-end speaker verification achieves the verification through estimating directly the
similarity score between a pair of utterances, which is formulated as a binary (ie, target …

In this paper, we describe several techniques for improving the acoustic and language
model of an automatic speech recognition (ASR) system operating on code-switching (CS) …

The COVID-19 pandemic has caused the death of many people around the world and has
also caused economic problems for all countries in the world. In the literature, there are …

We propose BlackOut, an approximation algorithm to efficiently train massive recurrent
neural network language models (RNNLMs) with million word vocabularies. BlackOut is …

Abstract Convolutional Neural Networks (CNNs) have shown to yield very strong results in
several Computer Vision tasks. Their application to language has received much less …