chenyangh/leaqi

Active Imitation Learing with Noisy Guidance

Active Imitation Learing with Noisy Guidance

This repository implements the algorithms presented in the paper

Dependencies

• We advise the reader to use virtualenv so that installing dependencies is easy
• Note that the code only works on a single gpu and have not been tested for multi-gpu

Installation

python -m pip install -e .

Code Arguments

> python -u run.py --help
usage: main.py [-h] [--num_epochs NUM_EPOCHS] [--b B] [--seed SEED]
               [--alpha ALPHA] [--model_lr MODEL_LR] [--task {multi,ner,gym}]
               [--filename FILENAME] [--weak_feature]
               [--env {Ner-v0,Keyphrase-v0,Pos-v0}] [--no_apple_tasting]
               [--method {mm,smentropy,lc}] [--diff_clf_lr DIFF_CLF_LR]
               [--diff_clf_type {gradient,adj_prob,entropy}]
               [--diff_clf_th DIFF_CLF_TH] [--diff_clf_fn_g DIFF_CLF_FN_G]
               [--unbias_weight] [--betadistro {1,query}]
               [--ref_type {normal,random}]
               [--alg {dagger:strong,dagger:weak,leaqi}]
               [--query_strategy {active,passive,None,random}]

Running the code

To run the experiments, go to the directory leaqi/,

for the different environments:

• Keyphrase use the flag --env Keyphraes-v0
• Part-of-Speech use the flag --env Pos-v0
• Named entity recognition use the flag --env Ner-v0

for different instantions of our algorithm:

• Turning Apple Tasting off --no_apple_tasting
• Random Reference --ref_type normal

Commands to reproduce LeaQI results:

• Keyphrase , run python -u main.py --env Keyphraes-v0
• Part-of-Speech , run python -u main.py --env Pos-v0
• Named entity recognition, run python -u main.py --env Ner-v0

Commands to reproduce baseline results:

• Keyphrase Normal DAgger, run python -u main.py --env Keyphraes-v0 --alg dagger:strong --query_strategy passive
• Keyphrase Active DAgger, run python -u main.py --env Keyphraes-v0 --alg dagger:strong --query_strategy active
• Part-of-Speech Normal DAgger, run python -u main.py --env Pos-v0 --alg dagger:strong --query_strategy passive
• Part-of-Speech Active DAgger, run python -u main.py --env Pos-v0 --alg dagger:strong --query_strategy active
• Named entity recognition Normal DAgger, run python -u main.py --env Ner-v0 --alg dagger:strong --query_strategy passive
• Named entity recognition Active DAgger, run python -u main.py --env Ner-v0 --alg dagger:strong --query_strategy active

Empirical evaluation

Empirical evaluation on three sequential decision making problems: (left-column) English named entity recognition, (middle-column) English keyphrase extraction and (right-column) low-resource language part of speech tagging on Greek, Modern (el). The top-row shows the performance (f-score or accuracy) with respect to the number of words queried. The bottom-row shows the number words queried with respect to number of words seen