BERT-unsupervised-OOD

Code for ACL 2021 paper "Unsupervised Out-of-Domain Detection via Pre-trained Transformers" by Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng and Caiming Xiong. (https://arxiv.org/pdf/2106.00948.pdf)

Requirements

• Python 3.7.3
• PyTorch 1.2
• Transformers 2.7.0
• simpletransformers 0.22.1

I do notice this repo is not compatabile with the newest version of Transformers (4.6.1) and simpletransformers (0.61.6). I will try to address this issue in a new branch if requested.

Overview

To run the models

Use the command

python ood_main.py \
  --method MDF \
  --data_type clinic \
  --model_class bert

To run baselines, change MDF to one from single_layer_bert, tf-idf and MSP (should load a BCAD mdoel).

Fine-tuning BERT with BCAD and IMLM

In-domain Masked Language Model (IMLM)

python finetune_bert.py \
    --type finetune_imlm \ 
    --data_type clinic \ 
    --model_class bert

Binary classification with auxiliary dataset (BCAD)

python finetune_bert.py \
    --type finetune_binary \ 
    --data_type clinic \ 
    --model_class bert
    --load_path xxx

The load_path can be the output of IMLM fine-tuning. If no load_path is specified, then pre-trained bert model is used.

You can use the fine-tuned model for OOD detection by adding the load_path parameter, e.g.,

python ood_main.py \
  --method MDF \
  --data_type clinic \
  --model_class roberta \
  --load_path  ./models/roberta_clinic_ft_IMLM_BCAD

You can also downloaded our fine-tuned (Ro)BERT(a) (IMLM+BCAD) models for SST and CLINIC150 here.

Citations

@inproceedings{xu-etal-2021-unsupervised,
    title = "Unsupervised Out-of-Domain Detection via Pre-trained Transformers",
    author = "Xu, Keyang  and Ren, Tongzheng  and Zhang, Shikun  and Feng, Yihao  and Xiong, Caiming",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural    Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.85",
    doi = "10.18653/v1/2021.acl-long.85",
    pages = "1052--1061",
}