Computes monotonic attention distribution from choosing probabilities.

Monotonic attention implies that the input sequence is processed in an explicitly left-to-right manner when generating the output sequence. In addition, once an input sequence element is attended to at a given output timestep, elements occurring before it cannot be attended to at subsequent output timesteps. This function generates attention distributions according to these assumptions. For more information, see Online and Linear-Time Attention by Enforcing Monotonic Alignments.

p_choose_i Probability of choosing input sequence/memory element i. Should be of shape (batch_size, input_sequence_length), and should all be in the range [0, 1].
previous_attention The attention distribution from the previous output timestep. Should be of shape (batch_size, input_sequence_length). For the first output timestep, preevious_attention[n] should be [1, 0, 0, ..., 0] for all n in [0, ... batch_size - 1].
mode How to compute the attention distribution. Must be one of 'recursive', 'parallel', or 'hard'.

  • 'recursive' uses tf.scan to recursively compute the distribution. This is slowest but is exact, general, and does not suffer from numerical instabilities.
  • 'parallel' uses parallelized cumulative-sum and cumulative-product operations to compute a closed-form solution to the recurrence relation defining the attention distribution. This makes it more efficient than 'recursive', but it requires numerical checks which make the distribution non-exact. This can be a problem in particular when input_sequence_length is long and/or p_choose_i has entries very close to 0 or 1.
  • 'hard' requires that the probabilities in p_choose_i are all either 0 or 1, and subsequently uses a more efficient and exact solution.

A tensor of shape (batch_size, input_sequence_length) representing the attention distributions for each sequence in the batch.

ValueError mode is not one of 'recursive', 'parallel', 'hard'.