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[D] Temporal coherence in transformers ? Why Fixed length inputs in Al-Rfou(2018) ?

Why use fixed length sequences in transformer ? In what way and why does it effect the performance and training of transformer ? Why did they not use sequences of length <= some number ?

Any paper regarding this?

Also, while reading the paper on Transformer-XL (Dai et. al, 2019) they say,

“We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence”

Why can’t we learn dependencies with a normal transformer(Vaswani et. al) beyond a fixed length without disrupting temporal coherence?

I think temporal coherence gets disturbed when the input length becomes comparable to the length of embedding used for a single word/character because the embedding then doesn’t contain enough information to link the word embedding to all the previous length of this input sequence . Am i right ?

submitted by /u/Jeevesh88
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.