The Benefits of Attention for Document Classification

A couple of weeks ago, I presented Embed, Encode, Attend, Predict - applying the 4 step NLP recipe for text classification and similarity at PyData Seattle 2017. The talk itself was inspired by the Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models blog post by Matthew Honnibal, creator of the spaCy Natural Language Processing (NLP) Python toolkit. In it, he posits that any NLP pipeline can be constructed from these 4 basic operations and provides examples from two of his use cases. In my presentation, I use his recipe to construct deep learning pipelines for two other processes - document classification and text similarity.

Now I realize that it might seem a bit pathetic to write a blog post about a presentation about someone else's blog post. But the reason I even came up with the idea for the presentation was because Honnibal's idea of using these higher level building blocks struck me as being so insightful and generalizable that I figured that it would be interesting to use it on my own use cases. And I decided to do the blog post because I thought that the general idea of abstracting a pipeline using these 4 steps would be useful to people beyond those who attended my talk. I also hope to provide a more in-depth look at the Attend step here than I could during the talk due to time constraints.

Today, I cover only my first use case of document classification. As those of you who attended my talk would recall, I did not get very good results for the second and third use cases around document and text similarity. I have a few ideas that I am exploring at the moment. If they are successful, I will talk about them in a future post.

The 4 step recipe

For those of you who are not aware of the 4-step recipe, I refer you to Honnibal's original blog post for the details. But if you would rather just get a quick refresher, the 4 steps are as follows:

• Embed - converts an integer into a vector. For example, a sequence of words can be transformed through vocabulary lookup to a sequence of integers, each of which could be transformed into a fixed size vector represented by the word embedding looked up from third party embeddings such as word2vec or GloVe.
• Encode - converts a sequence of vectors into a matrix. For example, a sequence of vectors representing some sequence of words such as a sentence, could be sent through a bi-directional LSTM to produce a sentence matrix.
• Attend - reduces the matrix to a vector. This can be done by passing the matrix into an Attention mechanism that captures the most salient features of the matrix, thus minimizing the information loss during reduction.
• Predict - reduces a vector to a integer label. This would correspond to a fully connected prediction layer that takes a vector as input and returns a single classification label.

Of these steps, all but the Attend step is adequately implemented by most Deep Learning toolkits. My examples use Keras, a Python deep learning library. In Keras, the Embed step is represented by the Embedding layer where you initialize the weights from an external embedding; the Encode step can be implemented using a LSTM layer wrapped in a Bidirectional wrapper; and the Predict step is implemented with a Dense layer.

Experiment: Document Classification

These steps can be thought of as large logical building blocks for our NLP pipeline. A pipeline can be composed of zero or more of these steps. It is also important to realize that each of these steps has a naive, non deep learning equivalent. For example, the Embed step can be done using one-hot vectors instead of third party word embeddings; the Encode step can be done by just concatenating the vectors along their short axis; the Attend step can be done by averaging the component word vectors; and the Predict step can use an algorithm other than deep learning. Since I wanted to see the effect of each of these steps separately, I conducted the following set of experiments - the links lead out to Jupyter notebooks on Github.

The data for this experiment comes from the Reuters 20 newsgroups dataset. It comes as part of scikit-learn's datasets package. It is a collection of 180000 newsgroup postings pre-categorized into one of 20 newsgroups. Our objective is to build a classifier (or classifiers) that can predict the document's newsgroup category from its text.

• Embed and Predict (EP) - Here I treat a sentence as a bag of words and a document as a bag of sentences. So a word vector is created by looking it up against a GloVe embedding, a sentence vector is created by averaging its word vectors, and a document vector is created by averaging its sentence vectors. The resulting document vector is fed into a 2 layer Dense network to produce a prediction of one of 20 class.
• Embed, Encode and Predict (EEP) - We use a document classification hierarchy as described in this paper by Yang, et al.^[1]. Specifically, a sentence encoder is created that transforms integer sequences (from words in sentences) into a sequence of word vectors by looking up GloVe embeddings, then converts the sequence of word vectors to a sentence vector by passing it through a Bidirectional LSTM and capturing the context vector. This sentence encoder is embedded into the document network, which takes in a sequence of sequence of integers (representing a sequence of sentences or a document). The sentence vectors are passed into a Bidirectional LSTM encoder that outputs a document vector, again by returning only the context vector. This document vector is fed into a 2 layer Dense network to produce a category prediction.
• Embed, Encode, Attend and Predict #1 (EEAP#1) - In this network, we add an Attention layer in the sentence encoder as well as in the Document classification network. Unlike the previous network, the Bidirectional LSTM in either network returns the full sequences, which are then reduced by the Attention layer. This layer is of the first type as described below. Output of the document encoding is a document vector as before, so as before it is fed into a 2 layer Dense network to produce a category prediction.
• Embed, Encode, Attend and Predict #2 (EEAP#2) - The only difference between this network and the previous one is the use of the second type of Attention mechanism as described in more detail below.
• Embed, Encode, Attend and Predict #3 (EEAP#3) - The only difference between this network and the previous one is the use of the third type of Attention mechanism. Here the Attention layer is fed with the output of the Bidirectional LSTM as well as the output of a max pool operation on the sequence to capture the most important parts of the encoding output.

The results of the experiment are as follows. The interesting values are the blue bars, that represent the accuracy reported by each trained model on the 30% held out test set. As you would expect, the Bag of Words (EP) approach yields the worst results, around 71.4%, which goes up to 77% once we replace the naive encoding with a Bidirectional LSTM (EEP). All the models with Attention outperform these two models, and the best result is around 82.4% accuracy with the first Attend layer (EEAP#1).

Attention Mechanisms

I think one reason Keras doesn't provide an implementation of Attention is because different researchers have proposed slightly different variations. For example, the only toolkit I know that offers Attention implementations is Tensorflow (LuongAttention and BahdanauAttention), but both are in the narrower context of seq2seq models. Perhaps a generalized Attention layer is just not worth the trouble given all the variations and maybe it is preferable to build custom one-offs yourself. In any case, I ended up spending quite a bit of time understanding how Attention worked and how to implement it myself, which I hope to also share with you in this post.

Honnibal's blog post also offers a taxonomy of different kinds of attention. Recall that the Attend step is a reduce operation, converting a matrix to a vector, so the following configurations are possible.

• Matrix to Vector - proposed by Raffel, et al.^[2]
• Matrix to Vector (with implicit context) - proposed by Lin, et al.^[3]
• Matrix + Vector to Vector - proposed by Cho, et al.^[4]
• Matrix + Matrix to Vector - proposed by Parikh, et al.^[5]

Of these, I will cover the first three here since they were used for the document classification example. References to the papers where these were propsed are provided at the end of the post. I have tried to normalize the notation across these papers so it is easier to talk about them in relation with each other.

I ended up implementing them as custom layers, although in hindsight, I could probably have used Keras layers to compose them as well. However, that approach can be complex if your attention mechanism is complicated. If you want an example of how to do that, take a look at Spacy's implementation of decomposable attention used for sentence entailment.

There are many blog posts and articles that talk about how Attention works. By far the best one I have seen is this one from Heuritech. Essentially, the Attention process involves combining the input signal (a matrix) with some other signal (a vector) to find an alignment that tells us which parts of the input signal we should pay attention to. The alignment is then combined with the input signal to produce the attended output. Personally, I have found that it helps to look at a flow diagram to see how the signals are combined, and the equations to figure out how to implement the layer.

Matrix to Vector (Raffel)

This mechanism is a pure reduction operation. The input signal is passed through a tanh and a softmax to produce an alignment matrix. The dot product of the alignment and the input signal is the attended output.


Two things to note here is the presence of the learnable weights W and b. The idea is that the component will learn these values so as to align the input based on the task it is being trained for.

The code for this layer can be found in class AttentionM in the custom layer code.

Matrix to Vector (Lin)

This mechanism is also a pure reduction operation, since the input to the layer is a matrix and the output is a vector. However, unlike the previous mechanism, it learns an implicit context vector u, in addition to W and b, as part of the training process. You can see this by the presence of a u vector entering the softmax and in the formula for α_t.


Code for this Attention class can be found in the AttentionMC class in the custom layer code.

Matrix + Vector to Vector (Cho)

Unlike the previous two mechanisms, this takes an additional context vector that is explicitly provided along with the input signal matrix from the Encode step. This can be a vector that is generated by some external means that is somehow representative of the input. In our case, I just took the max pool of the input matrix along the time dimension. The process of creating the alignment vector is the same as the first mechanism. However, there is now an additional weight that learns how much weight to give to the provided context vector, in addition to the weights W and b.


Code for this Attention class can be found in the AttentionMV class in the code for the custom layers.

As you may have noticed, the code for the various custom layers is fairly repetitive. We declare the weights in the build() method and the computations with the weights and signals in the call() method. In addition, we support input masking via the presence of the compute_mask() method. The get_config() method is needed when trying to save and load the model. Keras provides some guidance on building custom layers, but a lot of the information is scattered around in Keras issues and various blog posts. The Keras website is notable, among other things, for the quality of its documentation, but somehow custom layers haven't received the same kind of love and attention. I am guessing that perhaps it is because this is closer to the internals and hence more changeable, so harder to maintain, and also once you are doing custom layers, you are expected to be able to read the code yourself.

So there you have it. This is Honnibal's 4-step recipe for deep learning NLP pipelines, and how I used it for one of the use cases I talked about at PyData. I hope you found the information about Attention and how to create your own Attention implementations useful.

References

1. Yang, Z, et al (2016). Hierarchical attention networks for document classification. In Proceedings of NAACL-HLT (pp. 1480-1489).
2. Raffel, C, & Ellis, D. P (2015). Feed-forward networks with attention can solve some long term memory problems. arXiv preprint arXiv:1512.08756.
3. Lin, Z., et al. (2017). A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130.
4. Cho, K, et al. (2015). Describing multimedia content using attention-based encoder-decoder networks. IEEE Transactions on Multimedia, 17(11), 1875-1886.
5. Parikh, A. P., et al (2016). A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933.