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Repository Details

This repo provides code and data used in our TANDA paper.

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

We put together a script, data, and trained models used in our paper. In a nutshell, TANDA is a technique for fine-tuning pre-trained Transformer models sequentially in two steps:

  • first, transfer a pre-trained model to a model for a general task by fine-tuning it on a large and high-quality dataset;
  • then, perform a second fine-tuning step to adapt the transferred model to the target domain.

Script

We base our implementation on the transformers package. We use the following script to enable sequential fine-tuning option for the package.

git clone https://github.com/huggingface/transformers.git
cd transformers
git checkout f3386 -b tanda-sequential-finetuning
git apply tanda-sequential-finetuning-with-asnq.diff
  • f3386 is the latest commit as of Sun Nov 17 18:08:51 2019 +0900, and tanda-sequential-finetuning-with-asnq.diff is the diff to enable the option.

For example, to transfer with ASNQ and adapt with a target dataset:

  • download the ASNQ dataset and the target dataset (e.g. Wiki-QA, formatted similar as ASNQ), and
  • run the following script
python run_glue.py \
    --model_type bert \
    --model_name_or_path bert-base-uncased \
    --task_name ASNQ \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir [PATH-TO-ASNQ] \
    --per_gpu_train_batch_size 150 \
    --learning_rate 2e-5 \
    --num_train_epochs 2.0 \
    --output_dir [PATH-TO-TRANSFER-FOLDER]

python run_glue.py \
    --model_type bert \
    --model_name_or_path [PATH-TO-TRANSFER-FOLDER] \
    --task_name ASNQ \
    --do_train \
    --do_eval \
    --sequential \
    --do_lower_case \
    --data_dir [PATH-TO-WIKI-QA] \
    --per_gpu_train_batch_size 150 \
    --learning_rate 1e-6 \
    --num_train_epochs 2.0 \
    --output_dir [PATH-TO-OUTPUT-FOLDER]

Data

We use the following datasets in the paper:

Answer-Sentence Natural Questions (ASNQ)

  • ASNQ is a dataset for answer sentence selection derived from Google Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). The dataset details can be found in our paper.
  • ASNQ is used to transfer the pre-trained models in the paper, and can be downloaded here.
  • ASNQ-Dev++ can be downloaded here.

Domain Datasets

  • Wiki-QA: we used the Wiki-QA dataset from here and removed all the questions that have no correct answers.
  • TREC-QA: we used the *-filtered.jsonl version of this dataset from here.

Models

Models Transferred on ASNQ

TANDA: Models Transferred on ASNQ, then Fine-Tuned with Wiki-QA

TANDA: Models Transferred on ASNQ, then Fine-Tuned with TREC-QA

How To Cite TANDA

The paper appeared in the AAAI 2020 proceedings. Please cite our work if you find our paper, dataset, pretrained models or code useful:

@article{Garg_2020,
   title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
   volume={34},
   ISSN={2159-5399},
   url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
   DOI={10.1609/aaai.v34i05.6282},
   number={05},
   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
   publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
   author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
   year={2020},
   month={Apr},
   pages={7780–7788}
}

License Summary

The documentation, including the shared data and models, is made available under the Creative Commons Attribution-ShareAlike 3.0 Unported License. See the LICENSE file.

The sample script within this documentation is made available under the MIT-0 license. See the LICENSE-SAMPLECODE file.

Contact

For help or issues, please submit a GitHub issue.

For direct communication, please contact Siddhant Garg (sidgarg is at amazon dot com), Thuy Vu (thuyvu is at amazon dot com), or Alessandro Moschitti (amosch is at amazon dot com).

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