Statistical NLP (Group 17)

This is the repository for Group 17 of the Statistical Natural Language Processing module at UCL, formed by:

• Talip Ucar ([email protected])
• Adrian Daniel Szwarc ([email protected])
• Matthew Lee ([email protected])
• Adrian Gonzalez-Martin ([email protected])

This repository implements the Matching Networks architecture (Vinyals et al., 2016) in pytorch and applies it to a Language Modelling task. The architecture is flexible enough to allow easy experimentation with distance metrics, number of labels per episode, number of examples per label, etc.

More details can be found in the associated paper.

Demo

You can experiment with the model using the attached Colab Notebook.

One_Shot_Learning_for_Language_Modelling.ipynb

Getting Started

To keep the environments as reproducible as possible, we will use pipenv to handle dependencies. To install it just follow the instructions in https://pipenv.readthedocs.io/en/latest/.

The first time, to create the environment and install all required dependencies, just run:

$ pipenv install

This will create a virtualenv and will install all required dependencies.

Installing new dependencies

To add new dependencies just run:

$ pipenv install numpy

Remember to commit the updated Pipfile and Pipfile.lock files so that everyone else can also install them!

Folder Structure

Most of the source code can be found under the src/ folder. However, we also include a set of command line tools, which should help with sampling, training and testing models. These can be found under the bin/ folder.

Additionally, you can find the following folders:

• wikitext-2/: Raw WikiText-2 data set.
• data/: Pre-sampled set of label/sentence pairs and pre-generated vocabulary.
• models/: Pre-trained models. The filenames encode the different parameters used to train the model.
• results/: Data generated after evaluating the models. It includes predictions on the test set, embeddings and attention maps.
• figures/: Figures generated from the data in the results/ folder.

Tests

We are using pytest for writing and running unit tests. You can see some examples on the /src/tests/ folder.

To run all tests, just run the following command:

$ pytest -s src/tests

Dataset

On the data/ folder you can find the train.csv and test.csv files, which contain each 9000 labels with 10 examples each and 1000 labels with 10 examples each respectively.

The data is in CSV format with two columns:

• label: The word acting as label which we need to find.
• sentence: The sentence acting as input, where the particular word has been replaced with the token <blank_token>.

An example can be seen below:

label,sentence
music,no need to be a hipster to play <blank_token> in vynils
music,nowadays <blank_token> doesn't sound as before
...

Sampling new pairs

If you want to sample a new set of pairs from the WikiText-2 dataset you can use the bin.sample script. For example, to resample the entire dataset, we could just run:

$ python -m bin.sample -N 9000 -k 10 wikitext-2/wiki.train.tokens data/train.csv
$ python -m bin.sample -N 1000 -k 10 wikitext-2/wiki.test.tokens data/test.csv

Note that the file will be processed first, to be as similar as text coming from PTB.

Generating vocabulary

To make things easy to replicate, we generate in advance the vocabulary over the training set and store it in a file, which can then be used later for training and testing. You can have a look at the format in data/vocab.json.

To re-generate it (after sampling new pairs, for example), you can use the bin.vocab script:

$ python -m bin.vocab data/train.csv data/vocab.json

This command will store the vocabulary's state as a JSON file.

Training

Training of a new model can be performed using the bin.train script:

$ python -m bin.train -N 5 -k 2 -d euclidean data/vocab.json data/train.csv

The N and k parameters control the number of labels and examples we want per episode respectively. The other parameters refer to other parameters (like distance metric) and the pre-computed vocabulary and the training set.

After convergence, the best model's state_dict is stored under the models/ folder, with the different parameters encoded in its name. For example, the model poincare_vanilla_N=5_k=2_model_7.pth was trained using the poincare distance metric, vanilla embedding, using 5 labels with 2 examples each per episode. From the file name it can also be seen that it converged after 7 epochs.

These details are discussed in further detail in the associated paper.

Evaluation

Accuracy on a test set for a given model's snapshot can be measured using the bin.test script:

$ python -m bin.test -v data/vocab.json -m models/euclidean_vanilla_N\=5_k\=3_model_24.pth data/test.csv

This command has extra flags which allow to:

• -p: Store the predictions in the results/ folder.
• -e: Generate embeddings and attention for a single episode and store them in the results/ folder.

Some of the already generated data can be seen in the results/ folder.

Repository

This repository can be found in https://github.com/adriangonz/statistical-nlp-17.