CIS530_FP

CIS530 final project "Bag-of-BERT on SWAG"

README

Notice

• the SWAG data can be downloaded from https://github.com/rowanz/swagaf
• the datasets from GLUE can be downloaded from https://gluebenchmark.com/tasks
• the CommonsenseQA data can be downloaded from https://www.tau-nlp.org/commonsenseqa
• all the eval_results.txt contain the evaluation result of corresponding models.

SWAG / CommonsenseQA Multiple choice model with BERT

• usage:

  python swag_bert_baseline.py

  python csQA_bert_baseline.py

• optional parameter arguments:

  --data_dir input data directory (should contain the train.scv and val.csv )

  --output_dir directory for saving the BERT configurations the model weights, and the evaluation results

  --bert_model can be a string in the following: bert-base-uncased, bert-large-uncased, bert-base-cased, and bert-base-cased

  --max_seq_length the maximum total input sequence length after tokenization. Sequences longer than it will be truncated

  --do_train Boolean for whether to run the training steps

  --do_eval Boolean for whether to run the evaluation on val set

  --train_batch_size Default is 16

  --eval_batch_size Default is 16

  --learning_rate

  --num_train_epochs number of training epochs. Default is 3

  --seed random seed

Evaluation results

SWAG BERT Baseline

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.755

(Test set has no ground truth label)

SWAG BERT finetuned on MRPC

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.73

(Test set has no ground truth label)

SWAG BERT finetuned on MNLI

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.71

(Test set has no ground truth label)

SWAG BERT finetuned on SST-2

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.51

(Test set has no ground truth label)

Ensemble model of Baseline+MRPC+MNLI

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.7826

(Test set has no ground truth label)

BERT Baseline without context sentence in the input

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.655

(Test set has no ground truth label)

Hyperparameters

* batch size: 8
* BERT model: base, uncased
* maximum sequence length: 100
* training epochs: 3

Results

Dev set accuracy: 0.73

(Test set has no ground truth label)

CommonsenseQA

Hyperparameters

• batch size: 16
• BERT model: base, uncased
• maximum sequence length: 128
• training epochs: 4

Results

Dev set accuracy: 0.5348 (BERTBase implementation by University College London: 0.530)

(Test set has no ground truth label)

Adversarial Filtering

• get_candsents.py extracts the collection of choices from train set.
• get_cor_ids.py finds the indices of correct predictions in dev set.
• swag_ensemble_filter.py performs adversarial filtering using the ensemble model. usage: python swag_ensemble_filter.py --do_lower_case >filtered_pairs.txt

In \swag_bert_skpt folder:

• all_cands.txt contains the collection of choices extracted from train set.
• cor_ids.txt contains indices of all the correct predictions made by the ensemble model on dev set.
• filtered_pairs.txt contains the extraction result of potential substitutions, which is in the form of index in cor_ids.txt , index in dev set, context sentence , start of ending , correct answer , potential substitution.
• collect_stats.py shows the number of substitutions, average number of substitutions per sample, and the ratio of samples with at least one substitution.

Result

• BERT Baseline accuracy: 0.3388