Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
cfg
 
 
 
 
 
 
 
 
 
 
 
 

Harnessing the linguistic signal to predict scalar inferences

In this repository, we include the PyTorch implementation for predicting the implicature strength rating for some (but not all) as discussed in this paper.

Latest Version

  • Initial edits for cleaning-up the repository

Installation

Clone the repository and cd into the directory:

git clone https://github.com/yuxingch/Implicature-Strength-Some.git
cd Implicature-Strength-Some

To set up the virtual environment (python3) to run the script:

sudo pip install virtualenv        # You will need to do this only once
virtualenv -p python3 .env         # Creates a virtual environment with python3
source .env/bin/activate           # Activate the virtual environment
pip install -r requirements.txt    # Install all the dependencies
deactivate                         # Exit the virtual environment when you're done

Save some_database.csv, some_fulldataset.csv, and swbdext.csv in ./corpus_data/ directory:

- Implicature-Strength-Some
    - code
        - ...
    - corpus_data
        - some_database.csv
        - some_fulldataset.csv
        - swbdext.csv

Run Experiments

Preprocessing Dataset (Not required to run separately)

We don't have to run this separately. In the latest version, this has already been handled in run.py.

Splitting data into training/test sets directly (by default 70%/30%):

python ./code/split_dataset.py --seed=SEED_NUM --path=SAVE/PATH  --ratio=SPLIT_RATIO  --file=PATH/TO/CORPUS --verbose

Or, can use the default setting by running:

python ./code/split_dataset.py

Then the path to the training set will be ./datasets/train_db.csv and the path to the test set will be ./datasets/test_db.csv. We also have a ./datasets/all_db.csv that combines the previous two files.

Sample output with default settings (if verbose):

Spit data into training/test sets with split ratio=0.7
=====================
Using random seed 0, file loaded from ./corpus_data/some_fulldataset.csv
New files can be found in this directory: ./datasets
Out of total 1362 entries, 954 will be in training set and 408 will be in test set.
=====================

In actual runs, based on the implementation in run.py, the path will be ./{data_path}/seed_{cfg.SEED}/{train/test/all}_db.csv

Configuration File

In ./cfg/, there are example configuration files (e.g. ./cfg/cv_elmo_lstm_attn_context.yml) for our experiments. Using configuration files helps us simplify the process of feeding the experiment settings into the model. All tunable parameters are stored in run.py as cfg.

cfg.SOME_DATABASE = './some_database.csv' # where we load the dataset
cfg.CONFIG_NAME = ''                      # configuration name
cfg.RESUME_DIR = ''                       # path to the previous checkpoint we want to resume
cfg.SEED = 0                              # set random seed, default: 0
cfg.MODE = 'train'                        # train/test, default: train mode
cfg.PREDICTION_TYPE = 'rating'            # rating/strength, default: predict the implicature strength rating
cfg.MAX_VALUE = 7                         # max value in our raw data
cfg.MIN_VALUE = 1                         # min value in our raw data
cfg.IS_RANDOM = False                     # use random vectors to represent sentences, default: False
cfg.SINGLE_SENTENCE = True                # only use the target utterance
cfg.EXPERIMENT_NAME = ''                  # experiment name
cfg.OUT_PATH = './'                       # where we store the output (log, models, and etc.)
cfg.GLOVE_DIM = 100                       # GloVe dimension
cfg.IS_ELMO = True                        # use ELMo
cfg.IS_BERT = False                       # use BERT
cfg.ELMO_LAYER = 2
cfg.BERT_LAYER = 11
cfg.BERT_LARGE = False
cfg.ELMO_MODE = 'concat'                  # avg/concat, take the average of the ELMo vectors/concatenate the vectors
cfg.SAVE_PREDS = False                    # save the predictions as .csv file (in test mode only)
cfg.BATCH_ITEM_NUM = 30                   # number of examples in each batch
cfg.PREDON = 'test'                       # which set we want to make predictions on: train/test
cfg.CUDA = False                          # use GPU or not, default: False
cfg.GPU_NUM = 1                           # number of GPUs we use, default: 1
cfg.KFOLDS = 5                            # number of folds, default: 5
cfg.CROSS_VALIDATION_FLAG = True          # train with cross validation, default: True
cfg.SPLIT_NAME = ""

cfg.LSTM = edict()
cfg.LSTM.FLAG = False                     # whether using LSTM encoder or not
cfg.LSTM.SEQ_LEN = 30                     # the maximum sentence length, including `'<bos>'` and `'<eos>'`, default: 30
cfg.LSTM.HIDDEN_DIM = 512                 # LSTM hidden dimension
cfg.LSTM.DROP_PROB = 0.2                  # LSTM drop probability, when number of layers is > 1
cfg.LSTM.LAYERS = 2                       # number of LSTM layers
cfg.LSTM.BIDIRECTION = True               # use bidirectional LSTM or not
cfg.LSTM.ATTN = False                     # with attention layer, default: False

# Training options
cfg.TRAIN = edict()
cfg.TRAIN.FLAG = True                     # True/False, whether we're in training mode
cfg.TRAIN.BATCH_SIZE = 32                 # batch size
cfg.TRAIN.TOTAL_EPOCH = 200               # total number of epochs to run
cfg.TRAIN.INTERVAL = 4                    # save the checkpoint for every _ epochs
cfg.TRAIN.START_EPOCH = 0                 # starting epoch
cfg.TRAIN.LR_DECAY_EPOCH = 20             # decrease the learning rate for every ### epochs
cfg.TRAIN.LR = 5e-2                       # intial learning rate
cfg.TRAIN.COEFF = edict()
cfg.TRAIN.COEFF.BETA_1 = 0.9              # coefficient for Adam optimizer
cfg.TRAIN.COEFF.BETA_2 = 0.999            # coefficient for Adam optimizer
cfg.TRAIN.COEFF.EPS = 1e-8                # coefficient for Adam optimizer
cfg.TRAIN.LR_DECAY_RATE = 0.8             # decay rate for the learning rate
cfg.TRAIN.DROPOUT = edict()
cfg.TRAIN.DROPOUT.FC_1 = 0.75             # drop out prob in fully connected layer 1
cfg.TRAIN.DROPOUT.FC_2 = 0.75             # drop out prob in fully connected layer 2

cfg.EVAL = edict()
cfg.EVAL.FLAG = False
cfg.EVAL.BEST_EPOCH = 100

We can use the command-line argument to specify the path to the configuration file (see next section).

Start training

To use new parameters coming from the configuration file (e.g. ./cfg/cv_elmo_lstm_attn_context.yml), first make sure the file is in ./cfg/. Then use this line to run the script:

python ./code/run.py --conf='./cfg/cv_elmo_lstm_attn_context.yml'

The outputs will be stored in this hierachy (some examples):

- Implicature_Strength_Some
    - bert_lstm_attn
        - ...
    - code
    - corpus_data
    - datasets
        - seed_0
            - bert_layer_11_lstm
                - embs_train_30.npy
                - len_train_30.npy
            - elmo_layer_2_lstm
                - embs_train_30.npy
                - len_train_30.npy 
            - glove_lstm
                - embs_train_30.npy
                - len_train_30.npy
            - all_db.csv
            - test_db.csv
            - train_db.csv
        - ...
    - elmo_lstm_attn
        - 1
            - Best Model
                - RNet_epoch_{the_best_x}.pth
            - Model
                - RNet_epoch_{x}.pth
        - ...
        - Logging
            -  train_log.txt
    - glove_lstm_attn
        - ...

If you use these models, please cite the following paper:

@article{schuster2019harnessing,
  title={Harnessing the linguistic signal to predict scalar inferences},
  author={Schuster, Sebastian and Chen, Yuxing and Degen, Judith},
  journal={arXiv preprint arXiv:1910.14254},
  year={2019}
}

About

No description, website, or topics provided.

Resources

Releases

No releases published

Packages

No packages published