Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

Abstract

In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closed-book QA models. However, it is also known to suffer from its large storage footprint due to its document corpus and index. Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-and-read open-domain QA system by up to 160x. Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size.

Paper (To appear in NAACL 2021)
Authors: Sohee Yang and Minjoon Seo
Live Web Demo
- The demo normally takes 1-3 seconds for inference in the default setting, but ocassionally becomes slow according to the server condition.
BibTeX:

@inproceedings{mininalrnr,
  title={Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering},
  author={Yang, Sohee and Seo, Minjoon},
  booktitle={NAACL},
  year={2021}
}

This repository contains the code of the Minimal Retrieve & Read QA System that ranked the first place in the human (manual) evaluation and the second place in the automatic evaluation on "Systems Under 500Mb Track" of the NeurIPS 2020 EfficientQA competition.

Web Demo

You can play with the QA system in the Live Web Demo. You can also dynamically change the inference setting by controlling the values of top_k and passage_score_weight. (The demo normally takes 1-3 seconds for inference in the default setting, but ocassionally becomes slow according to the server condition.)

top_k: sets the number of passages to retrieve and pass to the reader. The value must be a positive integer. Default value is set to 50. For the live web demo, the values are limited within the range [1, 100] to prevent freezing from reading too many passages.
passage_score_weight:
- When the default value null is used, only the passage with the highest ranking score is used to extract the answers from. This is the setting used in DPR.
- If a float value (λ ∈ [0, 1] highly recommended) is given, multiple passages are considered to select the answer. Specifically, answer spans from multiple passages are scored using the weighted sum of passage ranking scores and answer spans scores. The weighted sum is calculated as (1 - λ) (log P_start + log P_end) + 2λ log P_rank. Please refer to the paper for more details.

If you have Docker installed, you can also run the web demo on your local machine in five minutes using this command.

A. Introduction

This repository contains the code for an interactive web demo, code for inference on the questions in a file (and evaluation on the answers), links to the model graphs/checkpoints (models), links to the index and preprocessed corpus files (resources), and links to built docker images.

competition directory contains the code to build and run the minimal-sized docker container used for the EfficientQA competition. Typing du -h / in the launched container reports 484.68MB as its size. Please see E. Competition Setting: Build & Run for detail.
playground directory contains more practical, refactored code to play with that one can either run a web demo or run inference on a file using models built in different settings. Please see D. Playground: Build & Run for detail.

B. Local Web Demo Quickstart

To run the web demo on your local machine, run the following using docker:

docker run \
  -v /etc/localtime:/etc/localtime:ro \
  --oom-kill-disable=true \
  --env MODE=demo \
  -p 10001:10001 \
  soheeyang/minimal-rnr-qa:effqa-tfserving \
  /workspace/entrypoint.sh

Then, access http://localhost:10001 to play with the demo!

C. Pre-Built Docker Images

Available in https://hub.docker.com/r/soheeyang/minimal-rnr-qa

soheeyang/minimal-rnr-qa:$DATASET-$MODEL_TYPE
- $DATASET: [ effqa | nq | trivia ]
- $MODEL_TYPE: [ tfserving | tfserving_faiss | pytorch ]
soheeyang/minimal-rnr-qa:$DATASET-competition
- $DATASET: [ effqa | nq | trivia ]
- soheeyang/minimal-rnr-qa:effqa-competition is the docker container used for the EfficientQA challenge

The follwoing are descriptions for each of the options for DATASET and MODEL_TYPE.

$DATASET is used to select the model; The model trained on this dataset is selected. The value must be one of the followings.
- effqa trained on Natural Questions (NQ) train set, validation done on EfficientQA dev set
- nq trained on NQ train set, validation done on NQ dev set
- trivia trained on TriviaQA (Trivia) train set, validation done on Trivia dev set
$MODEL_TYPE is used to select the type of the chosen model. The value must be one of the followings.
- tfserving TensorFlow (TF) graph for TF Serving. Index is fused into the graph to perform efficient passage retrieval without additional library dependency. This is the setting used in the EfficientQA competition. CPU serving. Smallest system footprint.
- tfserving_faiss TF graph for TF Serving, but without index. It installs and makes use of FAISS to perform passage retrieval. CPU serving.
- pytorch PyTorch checkpoint. It installs and makes use of FAISS to perform passage retrieval. The model code can be found at playground/workspace/minimal_rnr/pytorch/model.py. Supports serving on both CPU & GPU. Largest system footprint.

D. Playground: Build & Run

You can skip steps 1 and 2 if you use the pre-built docker images.

1. Download the code and necessary resources

git clone https://github.com/clovaai/minimal-rnr-qa.git
cd minimal-rnr-qa/playground

wget https://dl.dropboxusercontent.com/s/l7034dttyp4bbf2/minrnr_playground_models.tar.gz
wget https://dl.dropboxusercontent.com/s/51g36ytprbcl3mv/minrnr_playground_resources.tar.gz

tar xvf minrnr_playground_models.tar.gz
tar xvf minrnr_playground_resources.tar.gz

2. Build docker image

# inside minimal-rnr-qa/playground

DATASET=effqa
MODEL_TYPE=tfserving

chmod a+x ./build.sh
./build.sh $DATASET $MODEL_TYPE

This command builds a docker image tagged as minimal-rnr-qa:$DATASET-$MODEL_TYPE.

3-1. Run web demo

docker run \
  -v /etc/localtime:/etc/localtime:ro \
  --oom-kill-disable=true \
  --env MODE=demo \
  -p 10001:10001 \
  minimal-rnr-qa:$DATASET-$MODEL_TYPE \
  /workspace/entrypoint.sh

-v /etc/localtime:/etc/localtime:ro sets the timezone of the container to be same with the host's.
--oom-kill-disable=true prevents kill by OOM.
--env MODE=demo [REQUIRED] runs a web demo.
-p $HOST_PORT:10001 [REQUIRED] sets the port of the web page. connects the port 10001 of the container to a port of the host.
minimal-rnr-qa:$DATASET-$MODEL_TYPE [REQUIRED] Tag of the built image.
/workspace/entrypoint.sh [REQUIRED] Entrypoint of the container.

3-2. Run inference on a file

Download input data

The input files for EfficientQA dev set, NQ dev & test set, and Trivia dev & test set can be downloaded at once.

INPUT_DIR=/tmp/minimal-rnr-qa
OUTPUT_DIR=/tmp/minimal-rnr-qa

mkdir -p $INPUT_DIR
mkdir -p $OUTPUT_DIR

wget -P $INPUT_DIR https://dl.dropboxusercontent.com/s/juh12j1z0ct3zeu/minrnr_datasets.tar.gz
tar xvf $INPUT_DIR/minrnr_datasets.tar.gz -C $INPUT_DIR --strip-components=1

Run inference

INPUT_FILE_NAME=NQ-open.efficientqa.dev.1.1.jsonl
OUTPUT_FILE_NAME=NQ-open.efficientqa.dev.1.1-predictions.jsonl
TOP_K=80
PASSAGE_W=0.8

docker run \
  -v /etc/localtime:/etc/localtime:ro \
  --oom-kill-disable=true \
  -v $INPUT_DIR:/input \
  -v $OUTPUT_DIR:/output \
  --env MODE=file \
  --env TOP_K=$TOP_K \
  --env PASSAGE_W=$PASSAGE_W \
  minimal-rnr-qa:$DATASET-$MODEL_TYPE \
  /workspace/entrypoint.sh \
  /input/$INPUT_FILE_NAME \
  /output/$OUTPUT_FILE_NAME

-v /etc/localtime:/etc/localtime:ro sets the timezone of the container to be same with the host's.
--oom-kill-disable=true prevents kill by OOM.
-v $INPUT_DIR:/input [REQUIRED] maps $INPUT_DIR of the host to /input in the container where the data is read from. This directory must have the file to run inference on.
-v $OUTPUT_DIR:/output [OPTIONAL] maps $OUTPUT_DIR of the host to /output in the container where the prediction result file is written. If not specified, the output prediction file is written only in the container.
--env MODE=demo [REQUIRED] runs inference on the given input file and outputs the predictions
--env TOP_K=$INT_VALUE [OPTIONAL] sets the number of passages to retrieve and pass to the reader. It must be an integer value. Default value is set to 50.
--env PASSAGE_W=$FLOAT_VALUE [OPTIONAL]
- If the option is not used (as default) or null is given as the value, only the passage with the highest ranking score is used to extract the answers from. This is the setting used in DPR.
- If the value is given, multiple passages are considered to select the answer. Specifically, answer spans from multiple passages are scored using the weighted sum of passage ranking scores and answer spans scores. The given value for this option must be λ ∈ [0, 1], and the weighted sum is calculated as (1 - λ) (log P_start + log P_end) + 2λ log P_rank. This value may be tuned on the validation set to slightly raise the end-to-end question answering accuracy.
minimal-rnr-qa:$DATASET_$MODEL_TYPE [REQUIRED] Tag of the built image.
/workspace/entrypoint.sh [REQUIRED] Entrypoint of the container.
/input/$INPUT_FILE_NAME [REQUIRED] Name of the file to run inference on. CSV or JSON Lines files are supported.
- CSV files must consist of row of question strings or question\t["answer_1", ..., "answer_n"].
- JSON Lines files must consist of rows of {"question": ...}, {"question": ..., "answers": ...}, or {"question": ..., "answer": ...}.
- If answers exist, Exact Match (EM) score is calculated and reported at the end of the inference.
/output/$OUTPUT_FILE_NAME [REQUIRED] Name of the output prediction result file. The file takes JSON Lines format. Please note that even if "answer" is given as the key for answers in the input file, it changes to "answers" in the prediction file for consistency and easier evaluation.

E. Competition Setting: Build & Run

You can skip steps 1 and 2 if you use the pre-built docker images.

1. Download the code and necessary resources

git clone https://github.com/clovaai/minimal-rnr-qa.git
cd minimal-rnr-qa/competition

wget https://dl.dropboxusercontent.com/s/s5fa4rgf48bhhkb/minrnr_competition_resources.tar.gz
wget https://dl.dropboxusercontent.com/s/utwzozvuret1sdo/minrnr_competition_models.tar.gz

tar xvf minrnr_competition_models.tar.gz
tar xvf minrnr_competition_resources.tar.gz

2. Build docker image

# inside minimal-rnr-qa/competition

DATASET=effqa

chmod a+x ./build.sh
./build.sh $DATASET

Values for $DATASET
- effqa: the model used in the challenge (Section 3 in the paper)
- nq: trained on Natural Questions (Appendix A.5 in the paper)
- trivia: trained on TriviaQA (Appendix A.5 in the paper)
This command builds a docker image tagged as minimal-rnr-qa:$DATASET-competition.

3. Prepare data (same as the above)

The input files for EfficientQA dev set, NQ dev & test set, and Trivia dev & test set can be downloaded at once.

INPUT_DIR=/tmp/minimal-rnr-qa
OUTPUT_DIR=/tmp/minimal-rnr-qa

mkdir -p $INPUT_DIR
mkdir -p $OUTPUT_DIR

wget -P $INPUT_DIR https://dl.dropboxusercontent.com/s/juh12j1z0ct3zeu/minrnr_datasets.tar.gz
tar xvf $INPUT_DIR/minrnr_datasets.tar.gz -C $INPUT_DIR --strip-components=1

4. Run

# The setting used for EfficientQA submission

INPUT_FILE_NAME=NQ-open.efficientqa.dev.1.1.jsonl
OUTPUT_FILE_NAME=NQ-open.efficientqa.dev.1.1-predictions.jsonl
TOP_K=80
PASSAGE_W=0.8

docker run \
  -v ${INPUT_DIR}:/input \
  -v ${OUTPUT_DIR}:/output \
  --env TOP_K=$TOP_K \
  --env PASSAGE_W=$PASSAGE_W \
  --network="none" \
  --oom-kill-disable=true \
  minimal-rnr-qa:$DATASET-competition \
  /submission.sh \
  /input/$INPUT_FILE_NAME \
  /output/$OUTPUT_FILE_NAME

Below are the parameters to reproduce each of the results of the last row in Table 3 (in the Appendix of the paper).
- EfficientQA dev
  - DATASET=effqa / TOP_K=80 / PASSAGE_W=null
  - INPUT_FILE_NAME=NQ-open.efficientqa.dev.1.1.jsonl (from this link)
  - While 34.33 is reported in the paper, the value changed to 34.55 after we rebuilt the TensorFlow graph w.r.t. refactoring. The model supported here is the latter one.
- NQ dev
  - DATASET=nq / TOP_K=100 / PASSAGE_W=null
  - INPUT_FILE_NAME=nq-dev.jsonl
- NQ test
  - DATASET=nq / TOP_K=90 / PASSAGE_W=null
  - INPUT_FILE_NAME=nq-test.jsonl
- Trivia dev
  - DATASET=trivia / TOP_K=100 / PASSAGE_W=null
  - INPUT_FILE_NAME=trivia-dev.jsonl
- Trivia test
  - DATASET=trivia / TOP_K=100 / PASSAGE_W=null
  - INPUT_FILE_NAME=trivia-test.jsonl

F. License

Copyright 2021-present NAVER Corp.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.