MRC4ERE++

The repository contains the code for Paper "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction", Accepted by IJCAI 2020.

If you find this repo helpful, please cite the following:

@inproceedings{zhao-etal-2020-asking,
    title = "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction",
    author = "Zhao, Tianyang  and
      Yan, Zhao  and
      Cao, Yunbo  and
      Li, Zhoujun",
    booktitle = "Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence",
    month = jan,
    year = "2021",
    address = "Kyoto, Japan",
    publisher = "International Joint Conferences on Artificial Intelligence",
    url = "https://www.ijcai.org/Proceedings/2020/0546.pdf",
    pages = "3948--3954"
}

Overview

In this paper, we improve the existing MRCbased entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning.

For example,

when extracting a person entity, we can construct diverse question as follows:

• Who is mentioned in the context?
• Find people mentioned in the context?
• Which words are person entities?

Contents

1. Experimental Results
2. Dependencies
3. Usage

Experimental Results

We evaluate the proposed method on two widely-used datasets for entity relation extaction: ACE05 and CoNLL04. Micro precision, recall and F1-score are used as evaluation metrics.

• Results on ACE 2005:

  Models	Enity P	Entity R	Entity F	Relation P	Relation R	Relation F
  Sun et al. (2018)	83.9 s	83.2	83.6	64.9	55.1	59.6
  Li et al. (2019)	84.7	84.9	84.8	64.8	56.2	**60.2 **
  MRC4ERE++	85.9	85.2	85.5	62.0	62.2	62.1 (+1.9)

• Results on CoNLL 2004:

  Models	Enity P	Entity R	Entity F	Relation P	Relation R	Relation F
  Zhang et al. (2017)	–	–	85.6	–	–	67.8
  Li et al. (2019)	89.0	86.6	87.8	69.2	68.2	**68.9 **
  MRC4ERE++	89.3	88.5	88.9	72.2	71.5	71.9 (+3.0)

Data Preparation

Dependencies

• Package dependencies:

python >= 3.6
PyTorch == 1.1.0
pytorch-pretrained-bert == 0.6.1 

• Download BERT-Base-UnCased, English pretrained model and unzip.

Usage

As an example, the following command trains the proposed mothod on CoNLL04.