Monotonic attention mechanism with Luong-style energy function.

attention_luong_monotonic(
  object,
  units,
  memory = NULL,
  memory_sequence_length = NULL,
  scale = FALSE,
  sigmoid_noise = 0,
  sigmoid_noise_seed = NULL,
  score_bias_init = 0,
  mode = "parallel",
  dtype = NULL,
  name = "LuongMonotonicAttention",
  ...
)

Arguments

object	Model or layer object
units	The depth of the query mechanism.
memory	The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
memory_sequence_length	(optional): Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
scale	boolean. Whether to scale the energy term.
sigmoid_noise	Standard deviation of pre-sigmoid noise. See the docstring for `_monotonic_probability_fn` for more information.
sigmoid_noise_seed	(optional) Random seed for pre-sigmoid noise.
score_bias_init	Initial value for score bias scalar. It's recommended to initialize this to a negative value when the length of the memory is large.
mode	How to compute the attention distribution. Must be one of 'recursive', 'parallel', or 'hard'. See the docstring for tfa.seq2seq.monotonic_attention for more information.
dtype	The data type for the query and memory layers of the attention mechanism.
name	Name to use when creating ops.
...	A list that contains other common arguments for layer creation.

Value

None

Details

This type of attention enforces a monotonic constraint on the attention distributions; that is once the model attends to a given point in the memory it can't attend to any prior points at subsequence output timesteps. It achieves this by using the _monotonic_probability_fn instead of softmax to construct its attention distributions. Otherwise, it is equivalent to LuongAttention. This approach is proposed in [Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck, "Online and Linear-Time Attention by Enforcing Monotonic Alignments." ICML 2017.](https://arxiv.org/abs/1704.00784)