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I have an input as text from which I want to extract parameters as given in example below.

Input:

"client need to pay penalty of 10%  of amount  if there is delay in project for more than 3 months"

and output:

penalty = 10% and delay = 3

assuming there are N number of such parameters.

Here I have thought of using encoder and decoder model . Where I use RNN as encoder for text input Now I wonder what would be decoder architecture that will output N parameters and their values. what is alternative architecture to solve this problem.

Thanks in advance

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1 Answer 1

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Some popular methods for POS-tagging are Hidden Markov Model (HMM) and Conditional Random Field (CRF) book.

But you could use any recurrent networks (RNN, LSTM, bi-LSTM), CNNs or Transformers, to process your sequence of embeddings.

One could also use BERT (or other pre-trained models) to perform Name Entity Recognition blog.

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  • $\begingroup$ This does not seem to answer the question, does it? $\endgroup$
    – noe
    Sep 8 at 7:27
  • $\begingroup$ I want to check how encoder/decoder architecture will fit in here. when input/output both sequence, I can use RNN for both encoder/decoder. $\endgroup$
    – user140192
    Sep 8 at 10:56

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