SPADE

Introduction

• This repository contains the source code of our recent publication Spatial Dependency Parsing for Semi-Structured Document Information Extraction. The paper is accepted at Findings of ACL 2021.

• SPADE♠️ (SPAtial DEpendency parsing) accepts 2D text (text segments and their xy-coordinates).

• SPADE♠ generates the graph that represents semi-structured documents (such as receipts, name cards, invoices).

Task

Setup

• The code is tested on NVIDIA-P40, NAME="Ubuntu", VERSION="16.04.6 LTS (Xenial Xerus)"

1. conda create --name spade python==3.7.10

2. conda activate spade

3. git clone [this-repo]

4. pip install -r requirements

5. Download data.tar.gz from here. The file also include the small model trained on CORD dataset.

   mv data.tar.gz [project-dir]
   tar xvfz data.tar.gz

6. Download pretrained multi-lingual bert

cd scripts
python download_pretrained_models.py

6. Test the code with the sample data (input: ./data/samples/cord_predict.json)

   bash scripts/predict_cord.sh

7. (Optional) Download funsd dataset

   bash scripts/preprocess_funsd.sh

Data

Input (type1)

• Example from CORD-dev (data/sample/cord_dev.jsonl)

  {
  "data_id": 0, 
  "fields": ["menu.cnt", "menu.discountprice", "menu.itemsubtotal", "menu.nm", "menu.price", "menu.sub_cnt", "menu.sub_nm", "menu.sub_price", "menu.unitprice", "menu.sub_num", "menu.discountprice", "menu.num", "menu.sub_discountprice", "menu.sub_etc", "menu.etc", "menu.vatyn", "menu.itemsubtotal", "menu.sub_unitprice", "sub_total.discount_price", "sub_total.service_price", "sub_total.subtotal_price", "sub_total.tax_price", "sub_total.tax_and_service", "sub_total.etc", "sub_total.othersvc_price", "total.total_price", "total.menuqty_cnt", "total.total_etc", "total.emoneyprice", "total.menutype_cnt", "total.cashprice", "total.changeprice", "total.creditcardprice", "void_menu.nm", "void_menu.cnt", "void_menu.price", "void_menu.unitprice", "void_total.total_price", "void_total.subtotal_price", "void_total.tax_price", "void_total.etc"],
  "field_rs": ["menu.nm", "sub_total.subtotal_price", "total.total_price", "void_menu.nm", "void_total.total_price"], 
  "text": ["1", "REAL", "GANACHE", "16,500", ...]  
  "label": [[[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 
  "coord": [[[176, 556], [194, 556], [194, 586], [176, 586]], [[202, 554], [266, 554], [266, 586], [202, 586]], [[272, 554], [372, 554], [372, 586], [272, 586]], [[580, 552], [664, 552], [664, 584], [580, 584]], [[176, 590], [194, 590], [194, 620], [176, 620]], [[204, 588], [252, 588], [252, 620], [204, 620]], [[258, 588], [320, 588], [320, 618], [258, 618]], [[580, 586], [664, 586], [664, 618], [580, 618]], [[176, 624], [194, 624], [194, 654], [176, 654]], [[202, 622], [280, 622], [280, 654], [202, 654]], [[286, 620], [360, 620], [360, 652], [286, 652]], [[580, 620], [666, 620], [666, 650], [580, 650]], [[200, 686], [348, 686], [348, 748], [200, 748]], [[498, 684], [670, 684], [670, 746], [498, 746]], [[202, 746], [266, 746], [266, 778], [202, 778]], [[580, 740], [668, 740], [668, 770], [580, 770]], [[195, 779], [375, 770], [378, 833], [198, 841]], [[524, 772], [672, 772], [672, 834], [524, 834]]], 
  "vertical": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
  "img_sz": {"width": 864, "height": 1296}, 
  "img_feature": null, 
  "img_url": null
  }

  • fields: a list of field types to be parsed.
  • field_rs: a list of representative field types that are used for inter-field grouping.
  • text: a list of text segments.
  • label: [label-s, label-g]
  • label-s: (n_field + n_text) x n_text adjacency matrix expressing rel-s (serialization). null when predicting.
  • label-g: (n_field + n_text) x n_text adjacency matrix expressing rel-g (grouping). null when predicting.
  • coord: a list ofxy-coord of text-box.
  • xy-coord: [xy-top-left, xy-top-right, xy-bottom-right, xy-bottom-left]
  • img_sz: an image size
  • img_feature: an image feature. Currently not used.
  • img_url: an image url.
• In the uploaded data.tar.gz, you can also find type0 data where the data is organized reflecting their original format. In this case, raw_data_input_type should be set to type0 and label is generated while loading the data.

Test output

CORD

{
  "test__avg_loss": 0.08372728526592255,
  "test__f1": 0.9101991060544494,
  "test__precision_edge_avg": 0.932888658103816,
  "test__recall_edge_avg": 0.9192414351541544,
  "test__f1_edge_avg": 0.9259259429039002,
  "test__precision_edge_of_type_0": 0.9672624647224836,
  "test__recall_edge_of_type_0": 0.9710993577635059,
  "test__f1_edge_of_type_0": 0.9691771137713262,
  "test__precision_edge_of_type_1": 0.8985148514851485,
  "test__recall_edge_of_type_1": 0.8673835125448028,
  "test__f1_edge_of_type_1": 0.882674772036474
}

• ave_loss: Average cross entropy loss
• f1: $F_1$ of parse.
• [precision|recall|f1]_edge_avg: An average precision, recall, and $F_1$ of dependency parsing.
• [precision|recall|f1]_edge_of_type[0|1]: Precision, recall, and $F_1$ of dependency parsing of individual types: type0 for rel-s and type for rel-g.

FUNSD

In addition to the scores shown in CORD example, it includes

{
  "p_r_f1_entity": [
    [
      0.59375,
      0.3114754098360656,
      0.40860215053763443
    ],
    [
      0.8152524167561761,
      0.7047353760445683,
      0.7559760956175299
    ],
    [
      0.8589341692789969,
      0.6674786845310596,
      0.7511994516792323
    ],
    [
      0.6359447004608295,
      0.4423076923076923,
      0.5217391304347826
    ]
  ],
  "p_r_f1_all_entity_ELB": [
    0.8016216216216216,
    0.635934819897084,
    0.7092300334768054
  ],
  "p_r_f1_link_ELK": [
    0.6720977596741344,
    0.3101503759398496,
    0.42443729903536975
  ]
}

• p_r_f1_entity: [ [p_r_f1_question], [p_r_f1_answer], [p_r_f1_header], [p_r_f1_others]] for the entity labeling task.
• p_r_f1_entity_ELB: The FUNSD entity labeling task precision, recall, and $F_1$ scores for all fields.
• p_r_f1_entity_ELK: The FUNSD entity linking task precision, recall, and $F_1$ scores for all fields.

Prediction output

{
    "data_id": "00081",
    "text_unit": ["1", "SU", "##RI", "##MI","29", ... ],
    "pr_parse": [
      [{"menu.nm": "SURIMI"}, {"menu.cnt": "1"}, {"menu.price": "29,091"}], 
      [{"menu.nm": "CREAMY CHK CLS FTC"}, {"menu.cnt": "1"}, {"menu.price": "42,727"}],
      [{"menu.nm": "MIX 4FUN CHOCOLATE"}, {"menu.cnt": "1"}], 
      [{"menu.nm": "GREEN ITSODA PITCHER"}, {"menu.price": "19,091"}, {"menu.cnt": "1"}], 
      [{"menu.nm": "SC/R GRILLED STEAK"}, {"menu.cnt": "1"}, {"menu.price": "99,091"}], 
      [{"sub_total.subtotal_price": "250,909"}, {"sub_total.tax_price": "25,091"}], 
      [{"total.total_price": "276,000"}]],
    "pr_label": [
      [
        [
          1,
          0,
          0,
          ...
         ],
         ... 
        ]
      ]
    "pr_text_unit_field_label": ["menu.cnt","menu.nm","menu.nm","menu.nm", "menu.price",...]
}

• data_id: A data id.

• text_unit: A list of tokens or text segments.

• pr_parse: A predicted parse.

• pr_label: A predicted adjacency matrices representing a dependency graph. Simliar to label in the input but each columm and row represents a text unit which is either token or text segment.

• pr_text_unit_field_label: A list of field-type label for each token in text_unit.

Preprocessing

CORD

• The preprocessed data and trained model is already included for CORD datset (type1).
• To generate them from from (almost) raw data (type0), do bash scripts/preprocess_cord.sh

FUNSD

• bash scripts/preprocess_funsd.sh

Model

Training

• bash scripts/train_[task].sh
• Takes around 4 days on 6 P40 gpus with DDP.
• The best model is picked using dev set for cord.
• For FUNSD task, use 'eary stopping' for the model validation. If the model is trained with uploaded config file with 6 P40, 2000-4000 epochs are recommended (some fluctuation in the final score is expected due to small size of the dataset depending on the random seed). Do not use the validation score for the model selection. It is dummy.

Evaluation

bash scripts/test_[task].sh

Prediction

bash scripts/predict_cord.sh

Citation

@inproceedings{hwang2021spade,
    title = "Spatial Dependency Parsing for Semi-Structured Document Information Extraction",
      author = {Wonseok Hwang and
               Jinyeung Yim and
               Seunghyun Park and
               Sohee Yang and
               Minjoon Seo},
    booktitle = "ACL",
    year = {2021}
}

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.