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Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality

To get low dimensional RoBERTA representations, see our blog post or the models section. The paper and the talk is on ACL anthology

To see examples of running steps used in the paper. Read on below.

Step 1: Extract Contextual Embeddings:

The DLATK first requires to tokenize the messages before generating the embeddings:

python3 dlatkInterface.py -d db -t table_name -c user_id --add_sent_tokenized

The embedding generation command:

CUDA_VISIBLE_DEVICES=0 python3 dlatkInterface.py -d db -t table_name -c user_id --add_emb \
--emb_model roberta-base --emb_layers 11 --emb_msg_aggregation mean --batch_size 30

table_name = {D_20, T_20}

Step 2: Run pre-trained dimension reduction tables using a specific method:

The DLATK command to extract the dimension reduction is done in two steps as explained in the report. The first step involved learning the reduction on the domain data and storing the learnt model in a pickle file:

python3 dlatkInterface.py -d db -t table_name -c user_id --group_freq_thresh 1000 \
-f 'feat$roberta_ba_meL11con$table_name$user_id$16to16' --model {dimred_model} \
--fit_reducer --k 128 --save_model --picklefile dimred_model_128.pickle

The number of dimensions to reduce to (components) can be changed by altering the argument of --k

The {dimred_model} here could be pca, nmf, fa, ae (for non linear auto-encoders). For ae, it is expected to prefix the command with CUDA_VISIBLE_DEVICES env variable to specify GPU.

The second step would be applying this learnt reduction model on the task data to generate the reduced representations.

python3 dlatkInterface.py -d db -t table_name -c user_id --group_freq_thresh 1000 \
-f 'feat$roberta_ba_meL11con$table_name$user_id$16to16' \
--transform_to_feats {dimred_table_name} --load --picklefile dimred_model_128.pickle

The name of the table to stored the dimension reduced representations is given in pace of dimred_table_name.

Step 3: Perform bootstrapped training and evaluation:

The commands to perform bootstrapped training followed by evaluation for regression task is given by:

python3 dlatkInterface.py -d db -t task_table_name -c user_id --group_freq_thresh 1000 \
-f '{feat_table_name}' --outcome_table 20_outcomes --outcomes age ext ope --train_reg \
--model ridgehighcv --train_bootstraps 10 --where 'r10pct_test_fold is NOT NULL' \
--train_bootstraps_ns 50 100 200 500 1000 --no_standardize \
--save_models --picklefile reg_model_{feat_table_name}.pickle

The feat table name is either the raw embeddings table name or the dimension reduced feature table name. The regression outcomes are listed in the --outcomes flag. The number of times to perform the bootstrapping is specified in --train_bootstraps flag and the sample sizes for bootstrapping is specifed in --train_bootstrap_ns flag. Finally the ridge model is saved as a pickle file.

python3 ~/NLP/dlatk/dlatkInterface.py -d db-t table_name -c user_id --group_freq_thresh 1000 \
-f '{feat_table_name}' --outcome_table 20_outcomes --outcomes age ext ope --predict_reg --where \
'facet_fold = 1' --load --picklefile reg_model_{feat_table_name}.pickle > output.txt

This command would store the evaluation result for the ten runs in output.txt.

For classification task the commands have a slight variation. The outcomes fag is changed to appropriate categorical column name. The --train_reg and --predict_reg are changed to --train_classifiers and --predict_classifiers respectively.

Results: Training sample size Vs Number of dimensions required

Number of training samples Demographic Tasks Personality Tasks Mental Health Tasks
50 16 16 16
100 128 16 22
200 512 32 45
500 768 64 64
1000 768 90 64

This work is intended to inform researchers in Computational Social Science a simple way to improve the performance of transformer based models. We find that training PCA on transformer representations using the domain data improves the model performance overall, with evidence of handling longer sequences better than other reduction methods. The table above presents a summary of systematic experiments, recommmending the number of dimensions required for given number of samples in each task domain to achieve the best performance.

You can cite our work with:

    @inproceedings{v-ganesan-etal-2021-empirical,
    title = "Empirical Evaluation of Pre-trained Transformers for Human-Level {NLP}: The Role of Sample Size and Dimensionality",
    author = "V Ganesan, Adithya  and Matero, Matthew  and Ravula, Aravind Reddy  andVu, Huy  and Schwartz, H. Andrew",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "aclanthology.org/2021.naacl-main.357/",
    pages = "4515--4532"}