This repo contains the code developed for the RepEval 2017 Shared Task by the the team Rivercorners.

You can read our report "Refining Raw Sentence Representations for Textual Entailment Recognition via Attention" here.

1. Set the DATA_PATH global variable in repeval/constants.py. This directory will contain subdirectories with different kinds of data (corpora, embeddings, etc.)
2. Set the DATABASE_PATH global variable in repeval/constants.py. A sqlite3 database will be automatically created in that path for storing run information
3. Create the directory corpus in DATA_PATH
4. Download the data from https://www.nyu.edu/projects/bowman/multinli/multinli_0.9.zip
5. Unzip the multnli_0.9.zip file in DATA_PATH/corpus/
6. Access the directory multnli_0.9/
7. Remove the _matched or _mismatched suffix from the dev file you want to use as validation. The resulting dev file should be named multinli_0.9_dev.jsonl
8. Create the directory word_embeddings in DATA_PATH/
9. Download the 840B 300d Glove embeddings from https://nlp.stanford.edu/data/glove.840B.300d.zip and extract the contents in DATA_PATH/word_embeddings/
10. Make sure you have pytorch for python 2.7 installed in your environment (we used pytorch 0.1.12)
11. Also install the packages tqdm for displaying progress bars and dataset for interfacing with the sqlite database. You can run pip install -r requirements.txt to install them automatically
12. Run the following command in the topmost directory of the package: python run_inner_att_model.py for training with default parameters

You can also run python run_inner_att_model.py --help for displaying a list of available hyperparameters. There are some experimental features such as --context_window_size and --pos_tags that will not work well in conjunction with other settings, but these are not necessary to replicate our results.

The first time run_inner_att.py is run it will create some pickle files in DATA_PATH/corpus/multinli_0.9/ for faster access in later runs. This will take some time.

Also, loading the Glove embeddings for the first time will take 10 minutes or so.

If you want to combine Stanford's SNLI corpus with the MultiNLI corpus follow these instructions:

1. Download the SNLI corpus from https://nlp.stanford.edu/projects/snli/snli_1.0.zip

2. Unzip snli_1.0.zip in DATA_PATH/corpus/

3. Modify the SNLI_PROPORTION_TO_SAMPLE variable in create_fullnli_dataset.py to fit your needs. The default is 0.15 to match the other genres' proportion. We did this following what the authors of the MultiNLI corpus did (link to their paper):

   We train models on SNLI, on MultiNLI, and on a mixture of both corpora. In the mixed setting, we use the full MultiNLI training set but down-sample SNLI by randomly selecting 15% of the SNLI training set at each epoch. This ensures that each available genre is seen with roughly equal frequency during training.

   Note that they sampled from the SNLI corpus at each epoch while we only do it once before beginning the training procedure.

4. Execute python create_fullnli_dataset.py. This will create the new directory DATA_PATH/corpus/fullnli/ following a structure similar to the original DATA_PATH/corpus/multinli_0.9/

5. Once these steps are completed you can choose which corpus to use when running your experiments, e.g. python run_inner_att_model.py --corpus FullNLICorpus

This code is intended to be run with python 2.7. It uses the following libraries:

• pytorch >= 0.1.12
• tqdm >= 4.11.2
• dataset >= 0.8.0

MIT

We also borrowed the Tree class from the nltk package (repeval/corpus/tree.py) licensed under the Apache License-2.0.