🦔 HEDGEhog 🦔: BERT-based multi-class uncertainty cues recognition

Description

The repo contains code for fine-tuning a pretrained language model (BERT, SciBERT, etc.) for the task of multi-class classification of uncertainty cues (a.k.a hedges).

You can use the code to either:

• train and evaluate your own model (see Train and evaluate); or
• use my fine-tuned model to generate predictions on your data (see Predict).

I use the Simple Transformers and W&B packages to perform the fine-tuning.

Contents

1. Setup
2. Data
3. Performance
4. Usage
   4.1 Train and evaluate
   4.2 Predict
5. References

Setup

The requirements are listed in the environment.yml file. It is recommended to create a virtual environment with conda (you need to have Anaconda or Miniconda installed):

$ conda env create -f environment.yml
$ conda activate hedgehog

Data

HEDGEhog is trained and evaluated on the Szeged Uncertainty Corpus (Szarvas et al. 2012¹). The original sentence-level XML version of this dataset is available here.

The token-level version that is used in the current repo can be downloaded from here in a form of pickled pandas DataFrame's. You can download either the split sets (train.pkl 137MB, test.pkl 17MB, dev.pkl 17MB) or the full dataset (szeged_fixed.pkl 172MB).

Each row in the df contains a token, its features (these are not relevant for HEDGEhog; they were used to train the baseline CRF model, see here), its sentence ID, and its label. The labels refer to different types of semantic uncertainty (Szarvas et al. 2012) -

• Epistemic: the proposition is possible, but its truth-value cannot be decided at the moment. Example: She may be already asleep.
• Investigation: the proposition is in the process of having its truth-value determined. Example: She examined the role of NF-kappaB in protein activation.
• Doxatic: the proposition expresses beliefs and hypotheses, which may be known as true or false by others. Example: She believes that the Earth is flat
• CoNdition: the proposition is true or false based on the truth-value of another proposition. Example: If she gets the job, she will move to Utrecht.
• Certain: the token is not an uncertainty cue.

Performance

Here is the performance of my downloadable fine-tuned model on the test set:

class	precision	recall	F1-score	support
Epistemic	0.90	0.85	0.88	624
Doxatic	0.88	0.92	0.90	142
Investigation	0.83	0.86	0.84	111
Condition	0.85	0.87	0.86	86
Certain	1.00	1.00	1.00	104,751
macro average	0.89	0.90	0.89	105,714

Usage

Train and evaluate

You can use the data and the code to train your own model, for example with another pretrained language model as basis or with different hyperparameters. To do this, follow the following steps:

1. Download the data and place train.pkl, test.pkl, dev.pkl in the data/ directory.
2. Add a dictionary with your new model args to the config.json file. See Simple Transformers for all the possible configuration options.
3. Adjust the --model_args, --model_type and --model_name parameters in train_model.py. You can either change the default values in the script or pass your arguments in the command line; for example -

$ python train_model.py --model_args my_new_args --model_type roberta --model_name roberta-base

To evaluate your model, use the evaluate_model.py script. Adjust the --model_type and --model_name parameters for your trained model, set the --output parameter to the path where you want to save the pickled model predictions. You can adjust the evaluate_model.py script to add additional evaluation metrics; see the docstring in the file and the Simple Transformers documentation for more details.

You can perform a sweep for hyperparameters optimization with the wandb_sweep.py script. See the docstring in the file, Simple Transformers documentation and W&B documentation for more details.

Predict

To use my fine-tuned model for generating predictions on your own data, follow the following steps:

1. Prepare your data in a pickled DataFrame which contains the column 'sentence'. For each row in the df, the text in 'sentence' will be split on space and a label will be predicted for each token. A list with the predicted labels will be saved in a new column named 'predictions'.
2. Download the hedgehog folder from here and place it in the models/ directory. The folder contains the model pytorch_model.bin and info about the tokenizer, the vocabulary and the configuration.
3. Run the predict.py script, indicating the path to your pickled data (alternatively, edit the default value in the script):

$ python predict.py --data_pkl ../data/mydata.pkl

References

¹ Szarvas, G., Vincze, V., Farkas, R., Móra, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. Computational Linguistics, 38(2), 335-367.