WARP Transfer: Initializes tokens and verbalizers using a hierarchical training approach which leads to significant improvement in results especially in low resource scenarios

Training setup for WARP Transfer

Prompt-based methods have recently gained popularity for their capability to generalize to multiple downstream tasks with high parameter efficiency. In this work, we improve these methods especially WARP by utilizing knowledge transfer between related tasks and thereby increase parameter efficiency in multi-domain scenarios, particularly in natural language understanding.

Setup

The code requires our custom fork of YerevaNN's internal version of allennlp

git clone https://github.com/asit2898/allennlp
git checkout warp
pip install .

Training

Training WARP-Transfer Stage 1

export DATASET="mnli"
export HPARAMS='{
    "benchmark":"glue",
    "classifier_init":null,
    "dataset":"'$DATASET'",
    "ensure_whitespace_between":false,
    "lr":0.001,
    "max_batch_size":8,
    "max_tokens_sq":262144,
    "num_epochs":30,
    "prompt_better_init":"<mask>",
    "prompts":[-10,-11,-12,-13,-14,null,-15,-16,-17,-18,-19,"<mask>",-20,-21,-22,-23,-24,null,-25,-26,-27,-28,-29],
    "seed":1,
    "transformer_model":"roberta-large"
}'
python -m allennlp train -s .aim/t-mnli-cb-joint configs/mnli_cb_joint.jsonnet

Training WARP-Transfer Stage 2

python -m allennlp train -s .aim/t-mnli-cb-stage-2 configs/mnli_init_cb_joint.jsonnet

WARP on NLU evaluation data

export DATASET="alarm"
export HPARAMS='{
    "classifier_init":null,
    "dataset":"nlu",
    "path": "'$DATASET'",
    "ensure_whitespace_between":false,
    "lr":0.001,
    "max_batch_size":8,
    "max_tokens_sq":262144,
    "num_epochs":30,
    "prompt_better_init":"<mask>",
    "prompts":[-10,-11,-12,-13,-14,null,-15,-16,-17,-18,-19,"<mask>",-20,-21,-22,-23,-24,null,-25,-26,-27,-28,-29],
    "seed":1,
    "transformer_model":"roberta-large"
}'
python -m allennlp train -s .aim/t-${DATASET} configs/nlu.jsonnet