Bidirectional Recurrent Neural Networks
=======================================
So far we assumed that our goal is to model the next word given what
we’ve seen so far, e.g. in the context of a time series or in the
context of a language model. While this is a typical scenario, it is not
the only one we might encounter. To illustrate the issue, consider the
following three tasks of filling in the blanks in a text:
1. ``I am _____``
2. ``I am _____ very hungry.``
3. ``I am _____ very hungry, I could eat half a pig.``
Depending on the amount of information available we might fill the
blanks with very different words such as *‘happy’*, *‘not’*, and
*‘very’*. Clearly the end of the phrase (if available) conveys
significant information about which word to pick. A sequence model that
is incapable of taking advantage of this will perform poorly on related
tasks. For instance, to do well in named entity recognition (e.g. to
recognize whether *Green* refers to *Mr. Green* or to the color)
longer-range context is equally vital. To get some inspiration for
addressing the problem let’s take a detour to graphical models.
Dynamic Programming
-------------------
This section serves to *illustrate* the problem. The specific technical
details do not matter for understanding the deep learning counterpart
but they help in motivating why one might use deep learning and why one
might pick specific architectures.
If we want to solve the problem using graphical models we could for
instance design a latent variable model as follows: we assume that there
exists some latent variable :math:`h_t` which governs the emissions
:math:`x_t` that we observe via :math:`p(x_t|h_t)`. Moreover, the
transitions :math:`h_t \to h_{t+1}` are given by some state transition
probability :math:`p(h_t|h_{t-1})`. The graphical model then looks as
follows:
.. figure:: ../img/hmm.svg
Hidden Markov Model.
For a sequence of :math:`T` observations we have thus the following
joint probability distribution over observed and hidden states:
.. math:: p(x,h) = p(h_1) p(x_1|h_1) \prod_{i=2}^T p(h_t|h_{t-1}) p(x_t|h_t)
Now assume that we observe all :math:`x_i` with the exception of some
:math:`x_j` and it is our goal to compute :math:`p(x_j|x^{-j})`. To
accomplish this we need to sum over all possible choices of
:math:`h = (h_1, \ldots, h_T)`. In case :math:`h_i` can take on
:math:`k` distinct values this means that we need to sum over
:math:`k^T` terms - mission impossible! Fortunately there’s an elegant
solution for this: dynamic programming. To see how it works consider
summing over the first two hidden variable :math:`h_1` and :math:`h_2`.
This yields:
.. math::
\begin{aligned}
p(x) & = \sum_h p(h_1) p(x_1|h_1) \prod_{i=2}^T p(h_t|h_{t-1}) p(x_t|h_t) \\
& = \sum_{h_2, \ldots h_T} \underbrace{\left[\sum_{h_1} p(h_1) p(x_1|h_1) p(h_2|h_1)\right]}_{=: \pi_2(h_2)}
p(x_2|h_2) \prod_{i=2}^T p(h_t|h_{t-1}) p(x_t|h_t) \\
& = \sum_{h_3, \ldots h_T} \underbrace{\left[\sum_{h_2} \pi_2(h_2) p(x_2|h_2) p(h_3|h_2)\right]}_{=: \pi_3(h_3)}
p(x_3|h_3) \prod_{i=3}^T p(h_t|h_{t-1}) p(x_t|h_t)
\end{aligned}
In general we have the *forward* recursion
.. math:: \pi_{t+1}(h_{t+1}) = \sum_{h_t} \pi_t(h_t) p(x_t|h_t) p(h_{t+1}|h_1)
The recursion is initialized as :math:`\pi_1(h_1) = p(h_1)`. In abstract
terms this can be written as :math:`\pi_{t+1} = f(\pi_t, x_t)`, where
:math:`f` is some learned function. This looks very much like the update
equation in the hidden variable models we discussed so far in the
context of RNNs. Entirely analogously to the forward recursion we can
also start a backwards recursion. This yields:
.. math::
\begin{aligned}
p(x) & = \sum_h \prod_{i=1}^{T-1} p(h_t|h_{t-1}) p(x_t|h_t) \cdot p(h_T|h_{T-1}) p(x_T|h_T) \\
& = \sum_{h_1, \ldots h_{T-1}} \prod_{i=1}^{T-1} p(h_t|h_{t-1}) p(x_t|h_t) \cdot
\underbrace{\left[\sum_{h_T} p(h_T|h_{T-1}) p(x_T|h_T)\right]}_{=: \rho_{T-1}(h_{T-1})} \\
& = \sum_{h_1, \ldots h_{T-2}} \prod_{i=1}^{T-2} p(h_t|h_{t-1}) p(x_t|h_t) \cdot
\underbrace{\left[\sum_{h_{T-1}} p(h_{T-1}|h_{T-2}) p(x_{T-1}|h_{T-1})\right]}_{=: \rho_{T-2}(h_{T-2})}
\end{aligned}
We can thus write the *backward* recursion as
.. math:: \rho_{t-1}(h_{t-1})= \sum_{h_{t}} p(h_{t}|h_{t-1}) p(x_{t}|h_{t})
with initialization :math:`\rho_T(h_T) = 1`. These two recursions allow
us to sum over :math:`T` variables in :math:`O(kT)` (linear) time over
all values of :math:`(h_1, \ldots h_T)` rather than in exponential time.
This is one of the great benefits of probabilistic inference with
graphical models. It is a very special instance of the `Generalized
Distributive
Law `__
proposed in 2000 by Aji and McEliece. Combining both forward and
backward pass we are able to compute
.. math:: p(x_j|x_{-j}) \propto \sum_{h_j} \pi_j(h_j) \rho_j(h_j) p(x_j|h_j).
Note that in abstract terms the backward recursion can be written as
:math:`\rho_{t-1} = g(\rho_t, x_t)`, where :math:`g` is some learned
function. Again, this looks very much like an update equation, just
running backwards unlike what we’ve seen so far in RNNs. And, indeed,
HMMs benefit from knowing future data when it is available. Signal
processing scientists distinguish between the two cases of knowing and
not knowing future observations as filtering vs. smoothing. See e.g. the
introductory chapter of the book by `Doucet, de Freitas and Gordon,
2001 `__
on Sequential Monte Carlo algorithms for more detail.
Bidirectional Model
-------------------
If we want to have a mechanism in RNNs that offers comparable look-ahead
ability as in HMMs we need to modify the recurrent net design we’ve seen
so far. Fortunately this is easy (conceptually). Instead of running an
RNN only in forward mode starting from the first symbol we start another
one from the last symbol running back to front. Bidirectional recurrent
neural networks add a hidden layer that passes information in a backward
direction to more flexibly process such information. The figure below
illustrates the architecture of a bidirectional recurrent neural network
with a single hidden layer.
.. figure:: ../img/birnn.svg
Architecture of a bidirectional recurrent neural network.
In fact, this is not too dissimilar to the forward and backward
recurrences we encountered above. The main distinction is that in the
previous case these equations had a specific statistical meaning. Now
they’re devoid of such easily accessible interpretaton and we can just
treat them as generic functions. This transition epitomizes many of the
principles guiding the design of modern deep networks - use the type of
functional dependencies common to classical statistical models and use
them in a generic form.
Definition
~~~~~~~~~~
Bidirectional RNNs were introduced by `Schuster and Paliwal,
1997 `__. For a
detailed discussion of the various architectures see also the paper by
`Graves and Schmidhuber,
2005 `__.
Let’s look at the specifics of such a network. For a given time step
:math:`t`, the mini-batch input is
:math:`\mathbf{X}_t \in \mathbb{R}^{n \times d}` (number of examples:
:math:`n`, number of inputs: :math:`d`) and the hidden layer activation
function is :math:`\phi`. In the bidirectional architecture: We assume
that the forward and backward hidden states for this time step are
:math:`\overrightarrow{\mathbf{H}}_t \in \mathbb{R}^{n \times h}` and
:math:`\overleftarrow{\mathbf{H}}_t \in \mathbb{R}^{n \times h}`
respectively. Here :math:`h` indicates the number of hidden units. We
compute the forward and backward hidden state updates as follows:
.. math::
\begin{aligned}
\overrightarrow{\mathbf{H}}_t &= \phi(\mathbf{X}_t \mathbf{W}_{xh}^{(f)} + \overrightarrow{\mathbf{H}}_{t-1} \mathbf{W}_{hh}^{(f)} + \mathbf{b}_h^{(f)}),\\
\overleftarrow{\mathbf{H}}_t &= \phi(\mathbf{X}_t \mathbf{W}_{xh}^{(b)} + \overleftarrow{\mathbf{H}}_{t+1} \mathbf{W}_{hh}^{(b)} + \mathbf{b}_h^{(b)}),
\end{aligned}
Here, the weight parameters
:math:`\mathbf{W}_{xh}^{(f)} \in \mathbb{R}^{d \times h}, \mathbf{W}_{hh}^{(f)} \in \mathbb{R}^{h \times h}, \mathbf{W}_{xh}^{(b)} \in \mathbb{R}^{d \times h}, and \mathbf{W}_{hh}^{(b)} \in \mathbb{R}^{h \times h}`
and bias parameters
:math:`\mathbf{b}_h^{(f)} \in \mathbb{R}^{1 \times h} and \mathbf{b}_h^{(b)} \in \mathbb{R}^{1 \times h}`
are all model parameters.
Then we concatenate the forward and backward hidden states
:math:`\overrightarrow{\mathbf{H}}_t` and
:math:`\overleftarrow{\mathbf{H}}_t` to obtain the hidden state
:math:`\mathbf{H}_t \in \mathbb{R}^{n \times 2h}` and input it to the
output layer. In deep bidirectional RNNs the information is passed on as
*input* to the next bidirectional layer. Lastly, the output layer
computes the output :math:`\mathbf{O}_t \in \mathbb{R}^{n \times q}`
(number of outputs: :math:`q`):
.. math:: \mathbf{O}_t = \mathbf{H}_t \mathbf{W}_{hq} + \mathbf{b}_q,
Here, the weight parameter
:math:`\mathbf{W}_{hq} \in \mathbb{R}^{2h \times q}` and bias parameter
:math:`\mathbf{b}_q \in \mathbb{R}^{1 \times q}` are the model
parameters of the output layer. The two directions can have different
numbers of hidden units.
Computational Cost and Applications
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
One of the key features of a bidirectional RNN is that information from
both ends of the sequence is used to estimate the output. That is, we
use information from future and past observations to predict the current
one (a smoothing scenario). In the case of language models this isn’t
quite what we want. After all, we don’t have the luxury of knowing the
next to next symbol when predicting the next one. Hence, if we were to
use a bidirectional RNN naively we wouldn’t get very good accuracy:
during training we have past and future data to estimate the present.
During test time we only have past data and thus poor accuracy (we will
illustrate this in an experiment below).
To add insult to injury bidirectional RNNs are also exceedingly slow.
The main reason for this is that they require both a forward and a
backward pass and that the backward pass is dependent on the outcomes of
the forward pass. Hence gradients will have a very long dependency
chain.
In practice bidirectional layers are used very sparingly and only for a
narrow set of applications, such as filling in missing words, annotating
tokens (e.g. for named entity recognition), or encoding sequences
wholesale as a step in a sequence processing pipeline (e.g. for machine
translation). In short, handle with care!
Training a BLSTM for the Wrong Application
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If we were to ignore all advice regarding the fact that bidirectional
LSTMs use past and future data and simply apply it to language models we
will get estimates with acceptable perplexity. Nonetheless the ability
of the model to predict future symbols is severely compromised as the
example below illustrates. Despite reasonable perplexity numbers it only
generates gibberish even after many iterations. We include the code
below as a cautionary example against using them in the wrong context.
.. code:: python
import d2l
from mxnet import nd
from mxnet.gluon import rnn
# Load data
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
# Define model
vocab_size, num_hiddens, num_layers, ctx = len(vocab), 256, 2, d2l.try_gpu()
lstm_layer = rnn.LSTM(num_hiddens, num_layers, bidirectional=True)
model = d2l.RNNModel(lstm_layer, len(vocab))
# Train
num_epochs, lr = 500, 1
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, ctx)
.. parsed-literal::
:class: output
Perplexity 1.2, 80053 tokens/sec on gpu(0)
time travellerererererererererererererererererererererererererer
travellerererererererererererererererererererererererererer
The output is clearly unsatisfactory for the reasons described above.
For a discussion of more effective uses of bidirectional models see
e.g. the sentiment classification in :numref:`chapter_sentiment_rnn`.
Summary
-------
- In bidirectional recurrent neural networks, the hidden state for each
time step is simultaneously determined by the data prior and after
the current timestep.
- Bidirectional RNNs bear a striking resemblance with the
forward-backward algorithm in graphical models.
- Bidirectional RNNs are mostly useful for sequence embedding and the
estimation of observations given bidirectional context.
- Bidirectional RNNs are very costly to train due to long gradient
chains.
Exercises
---------
1. If the different directions use a different number of hidden units,
how will the shape of :math:`\boldsymbol{H}_t` change?
2. Design a bidirectional recurrent neural network with multiple hidden
layers.
3. Implement a sequence classification algorithm using bidirectional
RNNs. Hint - use the RNN to embed each word and then aggregate
(average) all embedded outputs before sending the output into an MLP
for classification. For instance, if we have
:math:`(\mathbf{o}_1, \mathbf{o}_2, \mathbf{o}_3)` we compute
:math:`\bar{\mathbf{o}} = \frac{1}{3} \sum_i \mathbf{o}_i` first and
then use the latter for sentiment classification.
Scan the QR Code to `Discuss `__
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|image0|
.. |image0| image:: ../img/qr_bi-rnn.svg