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Google AI
Today we announce a novel, open-source method for text generation tasks (e.g., summarization or sentence fusion), which uses edit operations instead of generating text from scratch, leading to less errors and faster model execution. Read about it below.
Posted by Eric Malmi and Sebastian Krause, Software Engineers, Google Research Sequence-to-sequence (seq2seq) models have revolutionize...
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Thomas G. Dietterich 31. sij
Odgovor korisniku/ci @GoogleAI
You mean "fewer errors" :-)
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Eric Malmi 31. sij
Odgovor korisniku/ci @tdietterich @GoogleAI
This may have been avoided if the tweet was passed through the LaserTagger model that was trained for the GEC task!
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Dave 🦔 1. velj
Odgovor korisniku/ci @GoogleAI
I'm surprised this hasn't been done before! I know folks have done sequence tagging for extractive summarization at the level of deciding which sentences to keep. Is this the first work on doing the same at the token level? Or is it the focus on inference speed that's novel?
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Fernando Alva-Manchego 1. velj
Odgovor korisniku/ci @_dmh @GoogleAI
I haven't read the paper in detail yet, but I remember something similar for sentence simplification
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Vaclav Kosar 1. velj
Odgovor korisniku/ci @GoogleAI
Listen to the paper here you can:
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BELHABIB AMINE 1. velj
Odgovor korisniku/ci @GoogleAI @ZiyadMestour
this one is for u man
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Aritra Roy Gosthipaty 31. sij
Odgovor korisniku/ci @GoogleAI @moniseeta
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MOHAMMAD FAIZ 1. velj
Odgovor korisniku/ci @GoogleAI
This sounds good but it also contain lot of work and most importantly to apply what is been think or visualize...✌🙌
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wellactualybot 1. velj
Odgovor korisniku/ci @GoogleAI
um it's actualy spelled generatting*
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Serhat Bakis 1. velj
Odgovor korisniku/ci @GoogleAI @FT
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