<p>I think khaemuaset's answer is correct.</p>

<p>To reinforce: As I understand from the paper (I'm reading <a href="https://arxiv.org/pdf/1611.02344.pdf" rel="nofollow noreferrer">A Convolutional Encoder Model for Machine Translation</a>) and the corresponding Facebook AI Research PyTorch source code, the position embedding is a typical embedding table, but for seq position one-hot vectors instead of vocab one-hot vectors. I verified this with the source code <a href="https://github.com/pytorch/fairseq/blob/master/fairseq/modules/learned_positional_embedding.py" rel="nofollow noreferrer">here</a>. Notice the inheritance of <code>nn.Embedding</code> and the call to its <code>forward</code> method at line 32.</p>

<p>The class I linked to is used in the FConvEncoder <a href="https://github.com/pytorch/fairseq/blob/master/fairseq/models/fconv.py#L107" rel="nofollow noreferrer">here</a>.</p>
What is the position embedding in the convolutional sequence to sequence learning model?
<p>I don't understand the position embedding in paper Convolutional Sequence to Sequence Learning, anyone can help me? </p>