Skip to content

Latest commit

 

History

History
176 lines (110 loc) · 6.26 KB

README.md

File metadata and controls

176 lines (110 loc) · 6.26 KB
Raw

Automated Scoring for Reading Comprehension via In-context BERT Tuning

We present our (grand prize-winning) solution to the NAEP Automated Scoring Challenge for reading comprehension items. We develop a novel automated scoring approach based on meta-learning ideas via in-context tuning of language models. This repository contains the implementation of our best performing model Meta-trained BERT In-context and the BERT fine-tuning baseline from our paper Automated Scoring for Reading Comprehension via In-context BERT Tuning by Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard Baraniuk, and Andrew Lan published at AIED 2022.

For any questions please email or raise an issue.

If you find our code or paper useful, please consider citing:

@inproceedings{ fernandez2022AS,
  title={ Automated Scoring for Reading Comprehension via In-context {BERT} Tuning },
  author={ Fernandez, Nigel and Ghosh, Aritra and Liu, Naiming and Wang, Zichao and Choffin, Beno{\^{\i}}t and Baraniuk, Richard and Lan, Andrew },
  booktitle={ 23rd International Conference on Artificial Intelligence in Education (AIED 2022) },
  year={ 2022 }
}

Contents

  1. Installation
  2. Usage
  3. Dataset
  4. Code Structure
  5. Acknowledgements

Installation

Our code is tested with Python 3.7.4 in a virtual environment created using:

virtualenv -p python3 env

Install the dependencies listed in the requirements.txt file by running:

pip install -r requirements.txt

Usage

A Neptune account is required to log train-test-val information (loss, metrics, etc). Best model checkpoints are saved locally.

To train-val-test a BERT baseline model for fine-tuning, please run:

python run.py\
    --name "lm_base_response_only"\
    --task "[name]"\
    --lm "bert-base-uncased"\
    --batch_size 32\
    --neptune_project "[name]"\
    --data_folder [name]\
    --cross_val_fold 1\
    --cuda\
    --neptune_project [name]\
    --neptune

To train-val-test our Meta-trained BERT In-context model, please run:

python run.py\
    --name "meta_lm_incontext"\
    --task "meta_learning_via_lm_incontext_tuning"\
    --lm "bert-base-uncased"\
    --batch_size 32\
    --neptune_project "[name]"\
    --meta_learning\
    --num_test_avg 8\
    --num_val_avg 8\
    --data_folder [name]\
    --cross_val_fold 1\
    --cuda\
    --neptune_project [name]\
    --neptune

Argument information:

-h, --help Show this help message and exit

--name NAME Name of the experiment

--neptune_project NEPTUNE_PROJECT Name of the neptune project

--lm LM Base language model (provide any Hugging face model name)

--task TASK Item name (not required for meta learning via in-context tuning)

--demographic Use demographic information of student

--meta_learning Enable meta-learning via BERT in-context tuning

--num_test_avg NUM_TEST_AVG Number of different sets of randomly sampled examples per test datapoint to average score predictions

--num_val_avg NUM_VAL_AVG Number of different sets of randomly sampled examples per val datapoint to average score predictions

--num_examples NUM_EXAMPLES Number of in-context examples from each score class to add to input

--trunc_len TRUNC_LEN Max number of words in each in-context example

--lr_schedule LR_SCHEDULE Learning rate schedule to use

--opt {sgd,adam,lars} Optimizer to use

--iters ITERS Number of epochs

--lr LR Base learning rate

--batch_size BATCH_SIZE Batch size

--data_folder DATA_FOLDER Dataset folder name containing train-val-test splits for each cross validation fold

--cross_val_fold CROSS_VAL_FOLD Cross validation fold to use

--save_freq SAVE_FREQ Epoch frequency to save the model

--eval_freq EVAL_FREQ Epoch frequency for evaluation

--workers WORKERS Number of data loader workers

--seed SEED Random seed

--cuda Use cuda

--save Save model every save_freq epochs

--neptune Enable logging to Neptune

--debug Debug mode with less items and smaller datasets

--amp Apply automatic mixed precision training to save GPU memory

Dataset

For our experimentation, we used the training dataset provided by the NAEP Automated Scoring Challenge organizers. Please contact them for usage.

To run our approach for equivalent problems, the dataset structure expected is three separate train, validation and test json files, each containing a list of data samples represented as dictionaries [{key:value}]. Each data sample dictionary contains the following (key, value) pairs:

{'bl':[string], 'l1':[int], 'l2':[int], 'sx':[string], 'rc':[string], 'txt':[string]}

The keys above are:

'bl': Unique database like key to identify student response

'l1' : Score label by first human rater

'l2' : Score label by second human rater (set as -1 if not available)

'sx' : Sex of student (optional, required if demographic argument is true)

'rc' : Race of student (optional, required if demographic argument is true)

'txt' : Student response text to the reading comprehension item to be scored

Code Structure

models contains bert.py and meta_learning_via_bert_incontext_tuning.py implementing BERT and meta-trained BERT in-context models, respectively.

train contains train_bert.py and train_meta_learning_via_bert_incontext_tuning.py implementing training scripts for BERT and meta-trained BERT in-context, respectively.

utils contains batch_collator.py to collate batches for training, load_data.py to load train-val-test sets, and utils.py with general utility functions.

Acknowledgements

Fernandez, Ghosh, and Lan are partially supported by the National Science Foundation under grants IIS-1917713 and IIS-2118706.