A stack decoder for Tensorflow 2.x seq2seq models that generates N-Best.
I made it to retrieve N-best predicted sentences in the descending order of probability. The predicited sentences can be similar in terms of edit distance. I want the same output always, given the same input, and therefore tfa.seq2se2.BeamSearchDecoder is not suitable, as it uses sampling and designed to give variety of outputs.
The output will look like this:
Input: <mcdonald>
Predicted translatons with probability (Nbest):
[0.1748] <マクドナルド>
[0.1044] <マクダナルド>
[0.0557] <マックドナルド>
[0.0546] <マクダノルド>
[0.0400] <マクダナード>
It assumes the general RNN encoder-decoder framework.
The algorithm is based on the one given in Section 10.2 A^* ("Stack") Decoding in JM09.
For any inquiries, please contact: Shoichiro Yamanishi
from TFStackDecoder.stack_decoder import StackDecoder
from TFStackDecoder.stack_decoder import StackDecoderPath
# This is a replacement to evaluate() defined in the NMT tutorial
# It does not generate data for attention plots.
# It generates N-best sentences in the descending order of probability.
def evaluate_nbest(sentence):
sentence = preprocess_sentence(sentence)
inputs = [inp_lang.word_index[i] for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],
maxlen=max_length_inp,
padding='post')
inputs = tf.convert_to_tensor(inputs)
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([targ_lang.word_index['<start>']], 0)
BOS = [targ_lang.word_index['<start>']
EOS = [targ_lang.word_index['<end>']
# Here decoder is assumed to have been defined as a GRU decoder
# with BahdanauAttention.
stack_decoder = StackDecoder(decoder, BOS, EOS, use_attn=True)
BEAM_WIDTH = 20
NUM_NBEST = 5
MAX_LEN = max_length_targ + 2 # this is defined early in the NMT tutorial.
# + 2 is for BOS and EOS.
nbest_list = stack_decoder.NBest( enc_out, enc_hidden, BEAM_WIDTH, NUM_NBEST, MAX_LEN )
results = []
for r in nbest_list:
result = []
for i in r.sentence:
result.append( targ_lang.index_word[i] )
result = ' '.join(result)
results.append((r.log_prob, result))
return results, sentence-
JM09 : Speech and Language Processing, 2nd., Daniel Jurafsky & James H. Martin, 2009 Pearson Education