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Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (Findings of ACL: EMNLP 2020)

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Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT

Source code for learning Multi^2OIE for (multilingual) open information extraction.

Paper

Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
Youngbin Ro, Yukyung Lee, and Pilsung Kang*
Accepted to Findings of ACL: EMNLP 2020. (*corresponding author)


Overview

What is Open Information Extraction (Open IE)?

Niklaus et al. (2018) describes Open IE as follows:

Information extraction (IE) turns the unstructured information expressed in natural language text into a structured representation in the form of relational tuples consisting of a set of arguments and a phrase denoting a semantic relation between them: <arg1; rel; arg2>. (...) Unlike traditional IE methods, Open IE is not limited to a small set of target relations known in advance, but rather extracts all types of relations found in a text.

openie_overview

Note

Our Approach

multi2oie_overview

Step 1: Extract predicates (relations) from the input sentence using BERT

  • Conduct token-level classification on the BERT output sequence
    • Use BIO Tagging for representing arguments and predicates

Step 2: Extract arguments using multi-head attention blocks

  • Concatenate BERT whole hidden sequence, average vector of hidden sequence at predicate position, and binary embedding vector indicating the token is included in predicate span.
  • Apply multi-head attention operation over N times
    • Query: whole hidden sequence
    • Key-Value pairs: hidden states of predicate positions
  • Conduct token-level classification on the multi-head attention output sequence

Multilingual Extraction

  • Replace English BERT to Multilingual BERT
  • Train the model only with English data
  • Test the model in three difference languages (English, Spanish, and Portuguese) in zero-shot manner.

Usage

Prerequisites

  • Python 3.7

  • CUDA 10.0 or above

Environmental Setup

using 'conda' command,
# this makes a new conda environment
conda env create -f environment.yml
conda activate multi2oie
using 'pip' command,
pip install -r requirements.txt

Datasets

Original data file (bootstrapped sample from OpenIE4; used in SpanOIE) can be downloaded from here. Following download, put the downloaded data in './datasets' and use preprocess.py to convert the data into the format suitable for Multi^2OIE.

cd utils
python preprocess.py \
    --mode 'train' \
    --data '../datasets/structured_data.json' \
    --save_path '../datasets/openie4_train.pkl' \
    --bert_config 'bert-base-cased' \
    --max_len 64

For multilingual training data, set 'bert_config' as 'bert-base-multilingual-cased'.

Run the Code

We used TITAN RTX GPU for training, and the use of other GPU can make the final performance different.

for training,
python main.py [--FLAGS]
for testing,
python test.py [--FLAGS]

Model Configurations

# of Parameters

  • Original BERT: 110M
  • + Multi-Head Attention Blocks: 66M

Hyper-parameters {& searching bounds}

  • epochs: 1 {1, 2, 3}
  • dropout rate for multi-head attention blocks: 0.2 {0.0, 0.1, 0.2}
  • dropout rate for argument classifier: 0.2 {0.0, 0.1, 0.2, 0.3}
  • batch size: 128 {64, 128, 256, 512}
  • learning rate: 3e-5 {2e-5, 3e-5, 5e-5}
  • number of multi-head attention heads: 8 {4, 8}
  • number of multi-head attention blocks: 4 {2, 4, 8}
  • position embedding dimension: 64 {64, 128, 256}
  • gradient clipping norm: 1.0 (not tuned)
  • learning rate warm-up steps: 10% of total steps (not tuned)

Expected Results

Development set

OIE2016

  • F1: 71.7
  • AUC: 55.4

CaRB

  • F1: 54.3
  • AUC: 34.8

Testing set

Re-OIE2016

  • F1: 83.9
  • AUC: 74.6

CaRB

  • F1: 52.3
  • AUC: 32.6

References

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