Awesome

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (ACL 2021).

Please refer to this link for the code. The open-domain and CLINC150 datasets can be found at this link.

For training:

nohup python main.py --dataset_pos oos --dataset_neg squad --loss_ce_only --know_only --known_cls_ratio 0.75 --train_batch_size 200 --n_oos 200 --num_convex 400 --num_convex_val 200 --temp 0.1 --patient 100 --seed 888 --lr 1e-5 --num_train_epochs 1000 --datetime "20210401" --dl_large 1>oos.out 2>&1 &

Note that the training procedure can stop earlier than 1000 epochs, pls set a smaller patient number. We borrow the dataloader from ADB and their work is also for OOS intent detection, check it out if you are interested.

Apologies for that there is still a large proportion of legacy codes in our collaborator's link, but these codes have been commented out.