Episodic Memory in Lifelong Language Learning

Code for the paper: Episodic Memory in Lifelong Language Learning(Arxiv:1905.12926) for the text classification setup.

Introduction

The ability to continuously learn and accumulate knowledge throughout a lifetime and reuse it effectively to adapt to a new problem quickly is a hallmark of general intelligence. State-of-the-art machine learning models work well on a single dataset given enough training examples, but they often fail to isolate and reuse previously acquired knowledge when the data distribution shifts (e.g., when presented with a new dataset)—a phenomenon known as catastrophic forgetting. In this paper, the authors introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. Specificaly, they propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on the text classification and question answering tasks demonstrate that the episodic memory module is a crucial building block of general linguistic intelligence.

Model

Main components of the model are:

• Example Encoder:

  • Text Classification: x_t is a document to be classified; BERT produces a vector representation of each token in x_t, which includes a special beginning-of-document symbol CLS as x_t,0.
  • Question Answering: x_t is a concatenation of a context paragraph x_t^context and a question x_t^question separated by a special separator symbol SEP.

• Task Decoder:

  • Text classification: following the original BERT model, select the representation of the first token x_t,0 from BERT (i.e., the special beginning-of-document symbol) and add a linear transformation and a softmax layer to predict the class of x_t. The probability of the text being classified as class c is computed as:
    
  • Question Answering: The decoder predicts an answer span—the start and end indices of the correct answer in the context. The probability of each context token being the start of the answer is computed as:
    where x_t,m^context is the encoded representation of m^th token in the context.
    The probability of the end index of the answer analogously using w_end. The predicted answer is the span with the highest probability after multiplying the start and end probabilities.
    Note: To take into account that the start index of an answer needs to precede its end index by setting the probabilities of invalid spans to zero.

• Episodic Memory:

  • Architecture: The model is augmented with an episodic memory module that stores previously seen examples throughout its lifetime. The module is a key-value memory block. The key representation of x_t (denoted by _u) is obtained using a key network—which is a pretrained BERT model separate from the example encoder. The key network is freezed to prevent key representations from drifting as data distribution changes.
    Text Classification: The key is an encoded representation of the first token of the document to be classified, so u_t = x_t,0 (i.e., the special beginning-of-document symbol).
    Question Answering: The key is the encoded representation of the first token of the question, so u_t = x_t,0^question. For both tasks, the input and the label ⟨x_t , y_t ⟩ is stored as its associated memory value.
  • Write: In this paper, the authors have suggested to randomly decide whether to write a newly seen example into the memory with some probability.
  • Read: The memory has two retrieval mechanisms: (i) random sampling and (ii) K-nearest neighbors

Training and Inference

Illustration of training and inference:

Sparse experience replay:

At a certain interval(1% replay rate) throughout the learning period, uniformly sample from stored examples in the memory and perform gradient updates of the encoder-decoder network based on the retrieved examples. It helps mitigate catestophic forgetting.

Local Adaptation:

At inference time, given a test example, a query vector of the test example is obtained using the key network and query the memory to retrieve K nearest neighbors using the Euclidean distance function. These K examples are used to perform local adaptation. Gradient-based local adaptation to update parameters of the encoder-decoder model—denoted by W—to obtain local parameters W_i to be used for the current prediction as follows: 𝝀 is a hyperparameter, ⍺_k is the weight of the retrieved examples equal to 1/k. Note: W_i is only used to make a prediction for the i-th example, and the parameters are reset to W afterwards. In practice, only L local adaptation gradient steps are performed instead of finding the true minimum of the above equation.

Documents

1.Setup Instructions and Dependencies

You may setup the repository on your local machine by either downloading it or running the following line on terminal.

git clone https://github.com/h3lio5/episodic-lifelong-learning.git

All dependencies required by this repo can be downloaded by creating a virtual environment with Python 3.7 and running

python3 -m venv .env
source .env/bin/activate
pip install -r requirements.txt

2.Directory description

Download the data manually from link or to download the data automatically, run -

python data_download.py

The dataset files that you need to download and extract are as follows:

• yelp_review_full_csv.tar.gz
• yahoo_answers_csv.tar.gz
• ag_news_csv.tar.gz
• amazon_review_full_csv.tar.gz
• dbpedia_csv.tar.gz

Place the train and test csv files after renaming them according to their corresponding dataset names in the original_data subdirectory of the data/ directory.For instance, place training set of amazon under the original_data directory under the name.
The repository should like this after downloading and placing the data in the appropriate folders

root
├── README.md  
├── data
│   ├── ordered_data
│   │   ├── test
│   │   └── train
│   └── original_data
│       ├── test
│       │   ├── agnews.csv
│       │   ├── amazon.csv
│       │   ├── dbpedia.csv
│       │   ├── yahoo.csv
│       │   └── yelp.csv
│       └── train
│           ├── agnews.csv
│           ├── amazon.csv
│           ├── dbpedia.csv
│           ├── yahoo.csv
│           └── yelp.csv
├── data_loader.py
├── main.py
├── models
│   ├── MbPAplusplus.py
│   └── baselines
│       ├── MbPA.py
│       ├── enc_dec.py
│       └── replay.py
├── preprocess.py
└── requirements.txt

3.Preprocessing

To preprocess and create ordered datasets, run

python preprocess.py

4.Training Model from Scratch

To train your own model from scratch, run

python main.py --mode train --epochs "any_number" --order "1/2/3/4"

5.Inference

To test the model, run

python main.py --mode test --model_path "path_to_checkpoint" --memory_path "path_to_replay_memory"

References

• Original Paper Episodic Memory in Lifelong Language Learning (link)
• Used pretrained BERT model from the awesome huggingface.