initialising Seq2seq embedding with pretrained word2vec

Question

I am interested in initialising tensorflow seq2seq implementation with pretrained word2vec.

I have seen the code. It seems embedding is initialized

with          


        

Answer 1

You can change the tokanizer present in tensorflow/models/rnn/translate/data_utils.py to use a pre-trained word2vec model for tokenizing. The lines 187-190 of data_utils.py:

if tokenizer:
    words = tokenizer(sentence)
else:
    words = basic_tokenizer(sentence)

use basic_tokenizer. You can write a tokenizer method that uses a pre-trained word2vec model for tokenizing the sentences.