This project is part of the Natural Languange Understanding course (2019) at ETH.
Task: Find the best ending for a story (https://arxiv.org/abs/1604.01696).
Team: 25
| Name | |
|---|---|
| Ioannis Sachinoglou | saioanni@student.ethz.ch |
| Adamos Solomou | solomoua@student.ethz.ch |
| Anagnostidis Sotiris | sanagnos@student.ethz.ch |
| Georgios Vasilakopoulos | gvasilak@student.ethz.ch |
.
├── data
│ ├── glove-embeddings # 100d glove embeddings
│ ├── ROCStories # Datasets (train, validation, test)
│ ├── incorrect_endings # negative endings generated from the language model
│ ├── skip-thoughts # Skip thoughts embeddings for (train, validation, test)
├── results # Predictions for unlabeled test set
├── src # Source files
│ ├── bert # Files concerning running BERT classifier for the task.
│ ├── create_skip_thoughts_embeddings # Script to create skip thoughts embeddings.
├── report # Report pdf
└── README.md
- Install Python 3.6+
- Load modules and create virtual environment (works when running on eth leonhard cluster):
source initialize.sh - Install requirements and skip thought embeddings. Some experiments require skip thoughts embeddings as specified in the paper Skip-Thought Vectors (https://arxiv.org/abs/1506.06726 ,https://github.com/ryankiros/skip-thoughts). For time saving purposes these have been precomputed and are publicly available at https://polybox.ethz.ch/index.php/s/X3GsRxeIhATdt8J. They files saved have the form of a numpy array with a shape [num_samples, num_sentences, skip_thought_embeddings_size]. For the training set, each story has a total of 5 sentences, while for the validation and test set each story has 6 sentences (corresponding to the two possible endings). To install:
run_experiments setup
Run run_experiments help to display all available options.
Run run_experiments all to run all available models.
Run run_experiments bert to run BERT classifier.
The predcitions of the best performing model can be found in the results folder. Each line contains a number, 1 or 2, which corresponds the prediction of the model for the correct ending sentence.