question about cnn embedding dim and lstm dim

[zxgineng]

In code both cnn embedding dim and individual lstm outputs dim are 512.
The paper says it would compute a task specific weighting of all biLM layers.
The biLM layers embedding is concatenation of [foreward-lstm, backward-lstm] , so the dim should be 1024.
So how to compute a weighting between biLM layers embedding(1024) and cnn embedding(512)? How to add them with different dim

[matt-peters]

We duplicate and concatenate the CNN layer to form a 1024 dim vector before weighting with the [forward-lstm, backward-lstm] layers. You can see this here: https://github.com/allenai/bilm-tf/blob/master/bilm/model.py#L120