# CopyMTL

**Repository Path**: li_hai_ning/CopyMTL

## Basic Information

- **Project Name**: CopyMTL
- **Description**: AAAI2020一篇事件抽取的论文
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-04-02
- **Last Updated**: 2022-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

[Paper](https://arxiv.org/abs/1911.10438) accepted by AAAI-2020 

This is a followup paper of "Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism" ACL2018 [CopyRE](http://aclweb.org/anthology/P18-1047)

This repo only contains CopyRE' part. MTL part is very old and messy, we are not going to release it. 
In other words, this repo only uses the last token of the entity for training and evaluation. If you want CopyMTL to manipulate complete entities, we suggest using [pytorch-crf](https://pytorch-crf.readthedocs.io/en/stable/) to implement the sequence labeling module for encoder. The dataset from CopyRE does not support MTL as well, because it lose the NER annotation. You'll have to re-preprocessing the data from scratch to gain full entity, rather than the links below.


## Environment

python3

pytorch 0.4.0 -- 1.3.1

## Modify the Data path

This repo initially contain webnlg, you can run the code directly.
NYT dataset need to be downloaded and to be placed in proper path. see **const.py**.

The pre-processed data is avaliable in:

WebNLG dataset:
 https://drive.google.com/open?id=1zISxYa-8ROe2Zv8iRc82jY9QsQrfY1Vj

NYT dataset:
 https://drive.google.com/open?id=10f24s9gM7NdyO3z5OqQxJgYud4NnCJg3
 


## Run

`python main.py --gpu 0 --mode train --cell lstm --decoder_type one`

`python main.py --gpu 0 --mode test --cell lstm --decoder_type one`