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Title: Monotonic Chunkwise Attention

Paper content:
Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.
To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.
We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.
When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.
In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.
Sequence-to-sequence models BID27 BID5 ) with a soft attention mechanism have been successfully applied to a plethora of sequence transduction problems BID20 BID30 BID7 BID29 BID26 .
In their most familiar form, these models process an input sequence with an encoder recurrent neural network (RNN) to produce a sequence of hidden states, referred to as a memory.
A decoder RNN then autoregressively produces the output sequence.
At each output timestep, the decoder is directly conditioned by an attention mechanism, which allows the decoder to refer back to entries in the encoder's hidden state sequence.
This use of the encoder's hidden states as a memory gives the model the ability to bridge long input-output time lags BID24 , which provides a distinct advantage over sequence-to-sequence models lacking an attention mechanism .
Furthermore, visualizing where in the input the model was attending to at each output timestep produces an input-output alignment which provides valuable insight into the model's behavior.As originally defined, soft attention inspects every entry of the memory at each output timestep, effectively allowing the model to condition on any arbitrary input sequence entry.
This flexibility comes at a distinct cost, namely that decoding with a soft attention mechanism has a quadratic time and space cost O(T U ), where T and U are the input and output sequence lengths respectively.
This precludes its use on very long sequences, e.g. summarizing extremely long documents.
In addition, because soft attention considers the possibility of attending to every entry in the memory at every output timestep, it must wait until the input sequence has been processed before producing output.
This makes it inapplicable to real-time sequence transduction problems.
BID25 recently pointed out that these issues can be mitigated when the input-output alignment is monotonic, i.e. the correspondence between elements in the input and output sequence does not involve reordering.
This property is present in various real-world problems, such as speech recognition and synthesis, where the input and output share a natural temporal order (see, for example, fig. 2 ).
In other settings, the alignment only involves local reorderings, e.g. machine translation for certain language pairs BID3 .Based
on this observation, BID25 introduced an attention mechanism that explicitly enforces a hard monotonic input-output alignment, which allows for online and linear-time decoding. Figure
1: Schematics of the attention mechanisms discussed in this paper. Each node
represents the possibility of the model attending to a given memory entry (horizontal axis) at a given output timestep (vertical axis). (a) In soft
attention, the model assigns a probability (represented by the shade of gray of each node) to each memory entry at each output timestep. The context
vector is computed as the weighted average of the memory, weighted by these probabilities. (b) At test
time, monotonic attention inspects memory entries from left-to-right, choosing whether to move on to the next memory entry (shown as nodes with ×) or stop and attend (shown as black nodes). The context
vector is hard-assigned to the memory entry that was attended to. At the next
output timestep, it starts again from where it left off. (c) MoChA utilizes
a hard monotonic attention mechanism to choose the endpoint (shown as nodes with bold borders) of the chunk over which it attends. The chunk boundaries
(here, with a window size of 3) are shown as dotted lines. The model then performs
soft attention (with attention weighting shown as the shade of gray) over the chunk, and computes the context vector as the chunk's weighted average.However, the hard monotonicity constraint also limits the expressivity of the model compared to soft attention (which can induce an arbitrary soft alignment). Indeed, experimentally
it was shown that the performance of sequence-to-sequence models utilizing this monotonic attention mechanism lagged behind that of standard soft attention.In this paper, we aim to close this gap by introducing a novel attention mechanism which retains the online and linear-time benefits of hard monotonic attention while allowing for soft alignments. Our approach, which we
dub "Monotonic Chunkwise Attention" (MoChA), allows the model to perform soft attention over small chunks of the memory preceding where a hard monotonic attention mechanism has chosen to attend. It also has a training
procedure which allows it to be straightforwardly applied to existing sequence-to-sequence models and trained with standard backpropagation. We show experimentally
that MoChA effectively closes the gap between monotonic and soft attention on online speech recognition and provides a 20% relative improvement over monotonic attention on document summarization (a task which does not exhibit monotonic alignments). These benefits incur only
a modest increase in the number of parameters and computational cost. We also provide a discussion
of related work and ideas for future research using our proposed mechanism.
We have proposed MoChA, an attention mechanism which performs soft attention over adaptivelylocated chunks of the input sequence.
MoChA allows for online and linear-time decoding, while also facilitating local input-output reorderings.
Experimentally, we showed that MoChA obtains state-of-the-art performance on an online speech recognition task, and that it substantially outperformed a hard monotonic attention-based model on document summarization.
In future work, we are interested in applying MoChA to additional problems with (approximately) monotonic alignments, such as speech synthesis BID29 and morphological inflection BID1 .
We would also like to investigate ways to allow the chunk size w to also vary adaptively.
To facilitate building on our work, we provide an example implementation of MoChA online.

assistant: text: The paper proposes an extension to a previous monotonic attention model (Raffel et al 2017) to attend to a fixed-sized window up to the alignment position.