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GoldEn Retriever Icon GoldEn Retriever

This repository contains the authors' implementation of the EMNLP-IJCNLP 2019 paper "Answering Complex Open-domain Questions Through Iterative Query Generation".

It contains code for GoldEn (Gold Entity) Retriever, an iterative retrieve-and-read system that answers complex open-domain questions. This model answers complex questions that involve multiple steps of reasoning in an open-context open-domain setting (e.g., given the entire Wikipedia). GoldEn Retriever answers these questions by iterating between "reading" the context and generating natural language queries to search for supporting facts to read. It achieves competitive performance on the HotpotQA leaderboard without using powerful pretrained neural networks such as BERT. Below is an example of how this model answers a complex question by generating natural language queries at each step.

GoldEn Retriever model architecture

We also include in the prepared_data folder HotpotQA files generated by the GoldEn Retriever model/training procedure that can be used to train and evaluate few-document question answering systems. These QA systems can then be combined with GoldEn Retriever in the open-context open-domain setting.

Training Your Own GoldEn Retriever

Setting up

Checkout the code from our repository using

git clone --recursive

This will help you set up submodule dependencies (needed for DrQA). (Equivalently, you can do git submodule update --init --recursive after git clone)

This repo requires Python 3.6. Please check your shell environment's python before proceeding. To use ElasticSearch, make sure you also install Java Development Kit (JDK) version 8.

The setup script will download all required dependencies (python requirements, data, etc.) required to run the GoldEn Retriever pipeline end-to-end. Before running this script, make sure you have the Unix utility wget (which can be installed through anaconda as well as other common package managers). Along the way, it will also start running Elasticsearch and index the wikipedia dataset locally.

Note: This might take a while to finish and requires a large amount of disk space, so it is strongly recommended that you run this on a machine with at least 100GB of free disk space.


Run the model end-to-end

bash scripts/

By default, this generates predictions on the HotpotQA dev set in a directory named outdir. Take a look at the contents of the script for more details or to modify inputs/outputs/model/etc.

Training model components

Hop 1 query generator

  1. Generate oracle queries (labels) for the Hop 1 query generator

    python -m scripts.gen_hop1 dev && python -m scripts.gen_hop1 train

    This generates Hop 1 oracle queries under data/hop1

  2. Create dataset

    mkdir -p tmp
    python -m scripts.preprocess_hop1 --input_path <path_to_hotpot_hop1_train.json> --output_path ./tmp/hotpot_hop1_squad_train.json
    python -m scripts.preprocess_hop1 --input_path <path_to_hotpot_hop1_dev.json> --output_path ./tmp/hotpot_hop1_squad_dev.json
  3. Preprocess with DrQA format

    # In the DrQA dir
    python scripts/reader/ <path_to_tmp> <path_to_tmp> --split hotpot_hop1_squad_train --workers 4
    python scripts/reader/ <path_to_tmp> <path_to_tmp> --split hotpot_hop1_squad_dev --workers 4
  4. Sample training code

    python scripts/reader/ --embedding-file data/embeddings/glove.840B.300d.txt --tune-partial 500 --train-file <path_to_hotpot_hop1_squad_train-processed-corenlp.txt> --dev-file <path_to_hotpot_hop1_squad_dev-processed-corenlp.txt> --dev-json hotpot_hop1_squad_dev.json --hidden-size 128 --parallel True --data-workers 10 --batch-size 32 --test-batch-size 128 --learning-rate 0.001 --model-dir <path_to_model_tmp> --max-len 50 --model-name hop1_model
  5. Sample prediction code

    python scripts/reader/ data/datasets/hotpot_hop1

Hop 2 query generator

  1. Generate oracle queries (labels) for the Hop 2 query generator (note this has to be run after Hop 1 oracle queries have been generated)

    python -m scripts.gen_hop2 dev && python -m scripts.gen_hop2 train

    This generates Hop 2 oracle queries under data/hop2

  2. Create DrQA dataset

    Copy the hop2 label json files into DrQA/data/datasets folder, then

    python -m scripts.preprocess_hop2 <path_to_DrQA/data/datasets> hotpot_hop2_train.json
    python -m scripts.preprocess_hop2 <path_to_DrQA/data/datasets> hotpot_hop2_dev.json
  3. Preprocess with DrQA format

    # In the DrQA dir
    python scripts/reader/ data/datasets data/datasets --split SQuAD_hotpot_hop2_dev --workers 4
    python scripts/reader/ data/datasets data/datasets --split SQuAD_hotpot_hop2_train --workers 4
  4. Sample training code

    python scripts/reader/ --embedding-file data/embeddings/glove.840B.300d.txt --tune-partial 1000 --max-len 20 --train-file <path_to_SQuAD_hotpot_hop2_train-processed-corenlp.txt> --dev-file <path_to_SQuAD_hotpot_hop2_dev-processed-corenlp.txt> --dev-json <path_to_SQuAD_hotpot_hop2_dev.json> --model-dir <path_to_model_tmp> --model-name hop2_model --expand-dictionary False --num-epochs 40
  5. Sample prediction code

    python scripts/reader/ <path_to_SQuAD_hotpot_hop2_dev.json> --model <path_to_hop2_model> --embedding-file data/embeddings/glove.840B.300d.txt --out-dir data/datasets

BiDAF++ question answering component

  1. Generate QA data that is more compatible with the query generators using the oracle queries (note that this needs to be run after Hop 1 and Hop 2 query generation)

    python -m scripts.build_qa_data train && python -m scripts.build_qa_data dev-distractor
    # Optionally, run "python -m scripts.build_qa_data dev-fullwiki" to generate a dev set from the oracle queries where the gold paragraphs are not guanranteed to be contained

    This will generate training and dev sets that contain retrieved documents from Wikipedia with the oracle query under data/hotpotqa with the suffix _hops.json

  2. Preprocess the data for the BiDAF++ QA component

    # In the BiDAFpp directory
    python --mode prepro --data_file ../data/hotpotqa/hotpot_train_hops.json --para_limit 2250 --data_split train && python --mode prepro --data_file ../data/hotpotqa/hotpot_dev_distractor_hops.json --para_limit 2250 --data_split dev

    Note that the training set has to be preprocessed before the dev set.

  3. Train the BiDAF++ QA component

    python --mode train --para_limit 2250 --batch_size 64 --init_lr 0.001 --patience 3 --keep_prob .9 --sp_lambda 10.0 --period 20 --max_grad_norm 5 --hidden 128
  4. Sample code for predicting from the trained QA component

    python --mode prepro --data_file <input_file> --para_limit 2250 --data_split test --fullwiki # preprocess the input data
    python --mode test --data_split test --save <trained_model_directory> --prediction_file <output_file_name> --sp_threshold .33 --sp_lambda 10.0 --fullwiki --hidden 128 --batch_size 16


If you use GoldEn Retriever in your work, please consider citing our paper

  author={Qi, Peng and Lin, Xiaowen and Mehr, Leo and Wang, Zijian and Manning, Christopher D.},
  booktitle={2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing ({EMNLP-IJCNLP})},
  title={Answering Complex Open-domain Questions Through Iterative Query Generation},


All work contained in this package is licensed under the Apache License, Version 2.0. See the included LICENSE file.


Authors' implementation of EMNLP-IJCNLP 2019 paper "Answering Complex Open-domain Questions Through Iterative Query Generation"







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