MetaAdaptRank

This repository provides the implementation of meta-learning to reweight synthetic weak supervision data described in the paper Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision.

• 1. Contrastive Supervision Synthesis (CTSyncSup)
• 2. Meta Learning to Reweight CTSyncSup

CONTACT

For any question, please contact Si Sun by email [email protected] (respond to emails more quickly), we will try our best to solve ：）

QUICKSTART

python 3.7
Pytorch 1.5.0

0/ Data Preparation

First download and prepare the following data into the data folder:

• Download source-domain NLG training datasets and prepare them into the data/source_data folder.

  • MS MARCO Passage Ranking dataset [download]

  >> example data format

• Download target-domain inference/evaluation datasets and prepare them into the data/target_data folder.

  • clueweb09
  • robust04
  • trec-covid

  >> example data format

• Download the pre-trained language models and prepare them into the data/pretrain_model folder.

  • t5-small & t5-base (for Weak Supervision Synthesis)
  • bert-base-uncased (for ClueWeb09 and Robust04)
  • BiomedNLP-PubMedBERT-base-uncased-abstract (for TREC-COVID)

1 Contrastive Supervision Synthesis

1.1 Source-domain NLG training

• We train two query generators (QG & ContrastQG) with the MS MARCO dataset using train_nlg.sh in the run_shells folder:

  bash prepro_nlg_dataset.sh
  

• Optional arguments:

  --generator_mode            choices=['qg', 'contrastqg']
  --pretrain_generator_type   choices=['t5-small', 't5-base']
  --train_file                The path to the source-domain nlg training dataset
  --save_dir                  The path to save the checkpoints data; default: ../results
  

1.2 Target-domain NLG inference

• The whole nlg inference pipline contains five steps:

  • 1.2.1/ Data preprocess
  • 1.2.2/ Seed query generation
  • 1.2.3/ BM25 subset retrieval
  • 1.2.4/ Contrastive doc pairs sampling
  • 1.2.5/ Contrastive query generation

• 1.2.1/ Data preprocess. convert target-domain documents into the nlg format using prepro_nlg_dataset.sh in the preprocess folder:

  bash prepro_nlg_dataset.sh
  

• Optional arguments:

  --dataset_name          choices=['clueweb09', 'robust04', 'trec-covid']
  --input_path            The path to the target dataset
  --output_path           The path to save the preprocess data; default: ../data/prepro_target_data
  

• 1.2.2/ Seed query generation. utilize the trained QG model to generate seed queries for each target documents using nlg_inference.sh in the run_shells folder:

  bash nlg_inference.sh
  

• Optional arguments:

  --generator_mode            choices='qg'
  --pretrain_generator_type   choices=['t5-small', 't5-base']
  --target_dataset_name       choices=['clueweb09', 'robust04', 'trec-covid']
  --generator_load_dir        The path to the pretrained QG checkpoints.
  

• 1.2.3/ BM25 subset retrieval. utilize BM25 to retrieve document subset according to the seed queries using do_subset_retrieve.sh in the bm25_retriever folder:

  bash do_subset_retrieve.sh
  

• Optional arguments:

  --dataset_name          choices=['clueweb09', 'robust04', 'trec-covid']
  --generator_folder      choices=['t5-small', 't5-base']
  

• 1.2.4/ Contrastive doc pairs sampling. pairwise sample contrastive doc pairs from the BM25 retrieved subset using sample_contrast_pairs.sh in the preprocess folder:

  bash sample_contrast_pairs.sh
  

• Optional arguments:

  --dataset_name          choices=['clueweb09', 'robust04', 'trec-covid']
  --generator_folder      choices=['t5-small', 't5-base']
  

• 1.2.5/ Contrastive query generation. utilize the trained ContrastQG model to generate new queries based on contrastive document pairs using nlg_inference.sh in the run_shells folder:

  bash nlg_inference.sh
  

• Optional arguments:

  --generator_mode            choices='contrastqg'
  --pretrain_generator_type   choices=['t5-small', 't5-base']
  --target_dataset_name       choices=['clueweb09', 'robust04', 'trec-covid']
  --generator_load_dir        The path to the pretrained ContrastQG checkpoints.
  

2 Meta Learning to Reweight

2.1 Data Preprocess

• Prepare the contrastive synthetic supervision data (CTSyncSup) into the data/synthetic_data folder.

  • CTSyncSup_clueweb09
  • CTSyncSup_robust04
  • CTSyncSup_trec-covid

  >> example data format

• Preprocess the target-domain datasets into the 5-fold cross-validation format using run_cv_preprocess.sh in the preprocess folder:

  bash run_cv_preprocess.sh
  

• Optional arguments:

  --dataset_class         choices=['clueweb09', 'robust04', 'trec-covid']
  --input_path            The path to the target dataset
  --output_path           The path to save the preprocess data; default: ../data/prepro_target_data
  

2.2 Train and Test Models

• The whole process of training and testing MetaAdaptRank contains three steps:

  • 2.2.1/ Meta-pretraining. The model is trained on synthetic weak supervision data, where the synthetic data are reweighted using meta-learning. The training fold of the target dataset is considered as target data that guides meta-reweighting.

  • 2.2.2/ Fine-tuning. The meta-pretrained model is continuously fine-tuned on the training folds of the target dataset.

  • 2.2.3/ Ensemble and Coor-Ascent. Coordinate Ascent is used to combine the last representation layers of all fine-tuned models, as LeToR features, with the retrieval scores from the base retriever.

• 2.2.1/ Meta-pretraining using train_meta_bert.sh in the run_shells folder:

  bash train_meta_bert.sh
  

  Optional arguments for meta-pretraining:

  --cv_number             choices=[0, 1, 2, 3, 4]
  --pretrain_model_type   choices=['bert-base-cased', 'BiomedNLP-PubMedBERT-base-uncased-abstract']
  --train_dir             The path to the synthetic weak supervision data
  --target_dir            The path to the target dataset
  --save_dir              The path to save the output files and checkpoints; default: ../results
  

  Complete optional arguments can be seen in config.py in the scripts folder.

• 2.2.2/ Fine-tuning using train_metafine_bert.sh in the run_shells folder:

  bash train_metafine_bert.sh
  

  Optional arguments for fine-tuning:

  --cv_number             choices=[0, 1, 2, 3, 4]
  --pretrain_model_type   choices=['bert-base-cased', 'BiomedNLP-PubMedBERT-base-uncased-abstract']
  --train_dir             The path to the target dataset
  --checkpoint_folder     The path to the checkpoint of the meta-pretrained model
  --save_dir              The path to save output files and checkpoint; default: ../results
  

• 2.2.3/ Testing the fine-tuned model to collect LeToR features through test.sh in the run_shells folder:

  bash test.sh
  

  Optional arguments for testing:

  --cv_number             choices=[0, 1, 2, 3, 4]
  --pretrain_model_type   choices=['bert-base-cased', 'BiomedNLP-PubMedBERT-base-uncased-abstract']
  --target_dir            The path to the target evaluation dataset
  --checkpoint_folder     The path to the checkpoint of the fine-tuned model
  --save_dir              The path to save output files and the **features** file; default: ../results
  

• 2.2.4/ Ensemble. Train and test five models for each fold of the target dataset (5-fold cross-validation), and then ensemble and convert their output features to coor-ascent format using combine_features.sh in the ensemble folder:

  bash combine_features.sh
  

  Optional arguments for ensemble:

  --qrel_path             The path to the qrels of the target dataset
  --result_fold_1         The path to the testing result folder of the first fold model
  --result_fold_2         The path to the testing result folder of the second fold model
  --result_fold_3         The path to the testing result folder of the third fold model
  --result_fold_4         The path to the testing result folder of the fourth fold model
  --result_fold_5         The path to the testing result folder of the fifth fold model
  --save_dir              The path to save the ensembled `features.txt` file; default: ../combined_features
  

• 2.2.5/ Coor-Ascent. Run coordinate ascent using run_ranklib.sh in the ensemble folder:

  bash run_ranklib.sh
  

  Optional arguments for coor-ascent:

  --qrel_path             The path to the qrels of the target dataset
  --ranklib_path          The path to the ensembled features.
  

  The final evaluation results will be output in the ranklib_path.

Results

All TREC files listed in this paper can be found in Tsinghua Cloud.