debajyotidatta / multiNLI_mod

Baseline Models for MultiNLI Corpus. a fork from NYU-MLL group for the RepEval 2017 shared task.

The original version of the code was used to establish baselines for the MultiNLI corpus introduced in A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. I have added various approaches on top of the original version to learn about some encoding approaches and implement them . Thanks to the group at NYU, for sharing the code.

One specific change I want to highlight from the original version is that, the way the word-vectors were loaded in the original code was changed to a newer approach, to make it more memory efficient. This is because, while trying out embedding vectors for bigrams and trigrams, it resulted in the graphDef greater than 2GB error. The solution I implemented was from the stackoverflow post here.

Data

The MultiNLI and SNLI corpora are both distributed in JSON lines and tab separated value files. Both can be downloaded here.

Models

We present three baseline neural network models. These range from a bare-bones model (CBOW), to an elaborate model which has achieved state-of-the-art performance on the SNLI corpus (ESIM),

• Continuous Bag of Words (CBOW): in this model, each sentence is represented as the sum of the embedding representations of its words. This representation is passed to a deep, 3-layers, MLP. Main code for this model is in cbow.py
• Bi-directional LSTM: in this model, the average of the states of a bidirectional LSTM RNN is used as the sentence representation. Main code for this model is in bilstm.py
• Enhanced Sequential Inference Model (ESIM): this is our implementation of the Chen et al.'s (2017) ESIM, without ensembling with a TreeLSTM. Main code for this model is in esim.py
• Suggestions from the fast-text paper on bigrams and trigrams and using convolutional neural network and the selu activation on top of it.
• Sentence encoding approach from Hierarchical Attention Networks for Document Classification.

We use dropout for regularization in all the models.

Training and Testing

Training settings

The models can be trained on three different settings. Each setting has its own training script.

• To train a model only on SNLI data,

  • Use train_snli.py.
  • Accuracy on SNLI's dev-set is used to do early stopping.

• To train a model on only MultiNLI or on a mixture of MultiNLI and SNLI data using the n-gram approach,

  • Use train_mnli_ngram_wv.py.
  • The optional alpha flag determines what percentage of SNLI data is used in training. The default value for alpha is 0.0, which means the model will be only trained on MultiNLI data.
  • If alpha is a set to a value greater than 0 (and less than 1), an alpha percentage of SNLI training data is randomly sampled at the beginning of each epoch.
  • When using SNLI training data in this setting, we set alpha = 0.15.
  • Accuracy on MultiNLI's matched dev-set is used to do early stopping.

• To train a model on a single MultiNLI genre,

  • Use train_genre.py.
  • To use this training setting, you must call the genre flag and set it to a valid training genre (travel, fiction, slate, telephone, government, or snli).
  • Accuracy on the dev-set for the chosen genre is used to do early stopping.
  • Additionally, logs created with this training setting contain evaulation statistics by genre.
  • You can also train a model on SNLI with this script if you desire genre specific statistics in your logs.

Command line flags

To start training with any of the training scripts, there are a couple of required command-line flags and an array of optional flags. The code concerning all flags can be found in parameters.py. All the parameters set in parameters.py are printed to the log file everytime the training script is launched.

Required flags,

• model_type: there are three model types in this repository, cbow, bilstm, and cbow. You must state which model you want to use.
• model_name: this is your experiment name. This name will be used the prefix the log and checkpoint files.

Optional flags,

• datapath: path to your directory with MultiNLI, and SNLI data. Default is set to "../data"
• ckptpath: path to your directory where you wish to store checkpoint files. Default is set to "../logs"
• logpath: path to your directory where you wish to store log files. Default is set to "../logs"
• emb_to_load: path to your directory with GloVe data. Default is set to "../data"
• learning_rate: the learning rate you wish to use during training. Default value is set to 0.0004
• keep_rate: the hyper-parameter for dropout-rate. keep_rate = 1 - dropout-rate. The default value is set to 0.5.
• seq_length: the maximum sequence length you wish to use. Default value is set to 50. Sentences shorter than seq_length are padded to the right. Sentences longer than seq-length are truncated.
• emb_train: boolean flag that determines if the model updates word embeddings during training. If called, the word embeddings are updated.
• alpha: only used during train_mnli scheme. Determines what percentage of SNLI training data to use in each epoch of training. Default value set to 0.0 (which makes the model train on MultiNLI only).
• genre: only used during train_genre scheme. Use this flag to set which single genre you wish to train on. Valid genres are travel, fiction, slate, telephone, government, or snli.
• test: boolean used to test a trained model. Call this flag if you wish to load a trained model and test it on MultiNLI dev-sets* and SNLI test-set. When called, the best checkpoint will be used (see section on checkpoints for more details).

*Dev-sets are currently used for testing on MultiNLI since the test-sets have not be released.

Other parameters

Remaining parameters like the size of hidden layers, word embeddings, and minibatch can be changed directly in parameters.py. The default hidden embedding and word embedding size is set to 300, the minibatch size (batch_size in the code) is set to 32.

Sample commands

To execute all of the following sample commands, you must be in the "python" folder,

• To train on SNLI data only, here is a sample command,

  PYTHONPATH=$PYTHONPATH:. python train_snli.py cbow petModel-0 --keep_rate 0.9 --seq_length 25 --emb_train

  where the model_type flag is set to cbow and can be swapped for bilstm or esim, and the model_name flag is set to petModel-0 and can be changed to whatever you please.

• Similarly, to train on a mixture MultiNLI and SNLI data, here is a sample command,

  PYTHONPATH=$PYTHONPATH:. python train_mnli.py bilstm petModel-1 --keep_rate 0.9 --alpha 0.15 --emb_train

  where 15% of SNLI training data is randomly sampled at the beginning of each epoch.

• To train on just the travel genre in MultiNLI data,

  PYTHONPATH=$PYTHONPATH:. python train_genre.py esim petModel-2 --genre travel --emb_train

Testing models

On dev set,

To test a trained model, simply add the test flag to the command used for training. The best checkpoint will be loaded and used to evaluate the model's performance on the MultiNLI dev-sets, SNLI test-set, and the dev-set for each genre in MultiNLI.

For example,

PYTHONPATH=$PYTHONPATH:. python train_genre.py esim petModel-2 --genre travel --emb_train --test

With the test flag, the train_mnli.py script will also generate a CSV of predicitons for the unlabaled matched and mismatched test-sets.

Results for unlabeled test sets,

To get a CSV of predicted results for unlabeled test sets use predicitons.py. This script requires the same flags as the training scripts. You must enter the model_type and model_name, and the path to the saved checkpoint and log files if they are different from the default (the default is set ot ../logs for both paths).

Here is a sample command,

PYTHONPATH=$PYTHONPATH:. python predictions.py esim petModel-1 --alpha 0.15 --emb_train --logpath ../logs_keep --ckptpath ../logs_keep

This script will create a CSV with two columns: pairID and gold_label.

Checkpoints

We maintain two checkpoints: the most recent checkpoint and the best checkpoint. Every 500 steps, the most recent checkpoint is updated, and we test to see if the dev-set accuracy has improved by at least 0.04%. If the accuracy has gone up by at least 0.04%, then the best checkpoint is updated.

License

Copyright 2017, New York University

Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.