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Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

SpokenCSE 최신버젼 다운로드

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// repository documentation

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# Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
- [INTERSPEECH 2022 Paper](https://www.isca-speech.org/archive/pdfs/interspeech_2022/chang22c_interspeech.pdf)

![image](https://user-images.githubusercontent.com/2268109/169679335-6cf6f520-cf86-499f-9682-71484a394e55.png)

## Requirements
```
pip install -r requirements.txt
```
NOTE: 
- you may need additional setting for `phonemizer` if you want to do phoneme-related data preprocessing

## Usage

0. Data Preparation
For SLURP,
The preprocessed dataset is `datasets/slurp/slurp_with_oracle_test.json`.
The preprocessed dataset without filtering and separating test sets is `datasets/slurp/slurp.json`
The data preprocessing includes multiple operations including:
    - Derive ASR hypothesis
    - Generate phoneme sequences by `phonemizer`
    - Preprocess the dataset (1st version)
        - Scripts in `prepare_data` would you understand the process:
        - first run `make_golden_dataset` read only from data provided in SLURP repo
        - and then `make_dataset` would need transcriptions from different systems
    - Fine-tune `roberta-base` models on the 1st version dataset
    - Collect predictions and sub-sample the dataset with agreed pseudo label

For ATIS/TREC6 from [PhonemeBERT](https://github.com/Observeai-Research/Phoneme-BERT),
You can just clone their repo and unzip the dataset.

1. Contrastive Pretraining
```
python contrastive_pretraining.py
```

2. Fine-tuning
```
python finetune_on_slurp.py
```
or on the phonemebert datasets:
```
python finetune_on_phonemebert.py
```

Training and evaluation are both included in these two scripts.
Adjust the arguments as you need.

## Reference
Please cite the following paper:
```
@inproceedings{chang2022contrastive,
  title={Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding},
  author={Chang, Ya-Hsin and Chen, Yun-Nung},
  booktitle={The 23rd Annual Meeting of the International Speech Communication Association (INTERSPEECH)},
  pages={3458-3462},
  year={2022}
}
```

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