RNN-free TensorFlow model for reading comprehension on SQuAD
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README.md

Attention Is All SQuAD Needs

Overview

Effective reading comprehension models often rely on recurrent neural networks (RNNs) to capture positional dependencies in text. However, RNNs are sequential by construction, which limits the amount of parallel execution that can take place during training and inference. In Attention Is All You Need, the authors experiment with replacing RNN modules entirely with self-attention in a neural machine translation setting. Their replacement for recurrent cells is called the Transformer.

In the spirit of this research, we follow this paper and implement an RNN-free, attention-based model that performs competitively on the Stanford Question Answering Dataset (SQuAD). In particular, we use only convolution, dense layers, and self-attention, allowing our model to train ~4.5x faster than an analogous RNN-based model. This speedup comes without sacrificing effectiveness: A single model achieves 67.8/77.6 EM/F1 score, and an ensemble of 10 models achieves 70.6/79.6 EM/F1 score in official evaluation on the dev set.

Encoder Block

The main component of our model is called an Encoder Block. The following diagram shows a Transformer (left) and an Encoder Block (right). Note where the Encoder Block draws inspiration from the Transformer: The two modules are similar in their use of positional encoding, residual connections, layer normalization, self-attention sublayers, and feed-forward sublayers.

Alt text

An Encoder Block differs from the Transformer in its use of stacked convolutional sublayers, which use depthwise-separable convolution to capture local dependencies in the input sequence. Also note that the sublayer pre- and post-processing steps are rearranged. An Encoder Block uses layer norm in the pre-processing step, and performs dropout and adds the residual connection in the post-processing step.

Model

Our model, based off this paper, follows a grouping common to SQuAD models: An embedding layer, followed by encoding, context-query attention, modeling, and output layers.

Alt text

  1. Embedding Layer. The embedding layer maps context and query words to GloVe 300-dimensional word embeddings (Common Crawl 840B corpus), and maps characters to trainable 200-dimensional character embeddings. The character embeddings are passed through a convolutional layer and a max-pooling layer, as described in this paper, to produce 200-dimensional character-level word embeddings. We concatenate the word embeddings and pass them through a two-layer highway network, which outputs a 500-dimensional encoding of each input position.
  2. Encoding Layer. The encoding layer consists of a single Encoder Block, as shown in Figure 1, applied after a linear down-projection of the embeddings to size d_model = 128. An Encoder Block stacks B blocks of [C convolutional sublayers, a single self-attention sublayer, a feed-forward layer]. As mentioned previously, the convolutional sublayers use depthwise-separable convolution. Self-attention is implemented with multi-head, scaled dot-product attention as described in Attention Is All You Need, with 8 heads applied in parallel over the input. The feed-forward sublayer is a pair of time-distributed linear mappings (equivalently, convolutions with kernel size 1), with ReLU activation in between. For the encoding layer’s Encoder Block, each convolution has kernel size k = 7 and applies d_model = 128 filters. We set B = 1 and C = 4. We share weights across applications of the encoding layer to the context and question embeddings.
  3. Context-Query Attention Layer (BiDAF). The output of the encoding layer is fed to a bidirectional context-to-query (C2Q) and query-to-context (Q2C) layer, as described in this paper (BiDAF). This layer computes a similarity matrix S of shape (n, m), where n is the context length and m is the question length. The (i, j) entry of S is given by S(i,j) = W[c_i, q_j, c_i * c_j]. We apply softmax to the rows and multiply by the query vectors to get the C2Q attention. Similarly, we then apply softmax to the columns, multiply by the C2Q attention matrix, followed by the matrix of context vectors to get the Q2C attention. As in the BiDAF paper, we output [C, C2Q, C * C2Q, C * Q2C] from this layer.
  4. Modeling Layer. For the modeling layer, we use an Encoder Block with B = 7 blocks and C = 2 convolutional sublayers per block. We use k = 7 and d_model = 128, as in the encoding layer. We apply a single Encoder Block three times (i.e., weights are shared across the three applications), producing outputs M1, M2, and M3.
  5. Output Layer. For the output layer, we predict the answer start and end probability distributions independently with two heads, each performing a linear down-projection followed by softmax. The start predictor takes [M1, M2] as input, and the end predictor takes [M1, M3] as input.

At test time, a single model's predicted answer is the span (i, j) maximizing p_start(i) * p_end(j) subject to i <= j < i + 15. Our ensemble predictions use a majority voting strategy, where we take the span that is most commonly voted upon by the models. We break ties by taking the span with the highest predicted joint probability p_start(i) * p_end(j).

Training

For our optimizer, we use Adam with a learning rate that has an exponentially decreasing rate of increase from 0 to 0.001 for the first 1000 steps, followed by a fixed learning rate of 0.001 after step 1000. We use beta_1 = 0.8, beta_2 = 0.999, and epsilon = 1e-7 for the Adam hyperparameters and train with a batch size of 32.

During training, we apply multiple forms of regularization. We use L2 regularization on all trainable kernels with lambda = 3e-7, and we apply dropout with p_drop = 0.1 after every layer of the model (including both the word- and character-level embeddings). As a third form of regularization, we use stochastic depth dropout on every sublayer in an Encoder Block. In an Encoder Block with L total sublayers, during training the l-th sublayer has probability 1 - (l/L) * p_drop of survival (i.e., earlier sublayers are more likely to survive).

Results

We find that our model achieves over a four-fold speedup over an analogous RNN-based architecture. Moreover, our model is competitive on SQuAD. A single model achieves 67.8/77.6 EM/F1 score, and an ensemble of 10 models achieves 70.6/79.6 EM/F1 score in official evaluation on the dev set.

Usage

To train this model for yourself, first get access to a machine with a GPU. We recommend a GPU with at least 12 GB of memory, or you may have to decrease d_model to fit.

  1. Run git clone https://github.com/chrischute/squad-transformer.git.
  2. Navigate to the squad-transformer directory.
  3. Run ./setup_repo.sh to set up the virtual environment and download/pre-process the dataset and word vectors. This may take 30 minutes or longer, but only needs to be done once.
  4. Run source activate squad to activate the virtual environment.
  5. Run python code/main.py --mode=train --name=squad-transformer to begin training.

We suggest training in a separate tmux session, then launching another session to run TensorBoard. To run TensorBoard, navigate to squad-transformer/logs/ and run tensorboard --logdir=. --port=5678. Then you will be able to see plots in your browser at http://localhost:5678/ (if you're on a cloud instance, you may need to setup SSH port forwarding).

On an NVIDIA Tesla K80 GPU with batch size of 32, training proceeds at roughly 4,500 iterations per hour. Our model converges after ~30,000 iterations, giving a total training time of just under 7 hours.

Acknowledgements

This began as a final project for Stanford CS224n, and was supported by the Winter 2018 teaching staff. Microsoft Azure generously provided GPU instance credits used during development.