  We explore deep autoregressive Transformer models in language modeling for
speech recognition. We focus on two aspects. First, we revisit Transformer
model configurations specifically for language modeling. We show that well
configured Transformer models outperform our baseline models based on the
shallow stack of LSTM recurrent neural network layers. We carry out experiments
on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level
and 10K byte-pair encoding subword-level language modeling. We apply our
word-level models to conventional hybrid speech recognition by lattice
rescoring, and the subword-level models to attention based encoder-decoder
models by shallow fusion. Second, we show that deep Transformer language models
do not require positional encoding. The positional encoding is an essential
augmentation for the self-attention mechanism which is invariant to sequence
ordering. However, in autoregressive setup, as is the case for language
modeling, the amount of information increases along the position dimension,
which is a positional signal by its own. The analysis of attention weights
shows that deep autoregressive self-attention models can automatically make use
of such positional information. We find that removing the positional encoding
even slightly improves the performance of these models.
