Source code for Transformer-based Multi-task Learning for Disaster Tweet Categorisation (UCD's participation in TREC-IS 2020A, 2020B and 2021A).

Overview

Source code for "UCD participation in TREC-IS 2020A, 2020B and 2021A".

*** update at: 2021/05/25

This repo so far relates to the following work:

  • Transformer-based Multi-task Learning for Disaster Tweet Categorisation, (WiP paper, ISCRAM 2021)
  • Multi-task transfer learning for finding actionable information from crisis-related messages on social media, (paper, TREC 2020)

Setup

git clone https://github.com/wangcongcong123/crisis-mtl.git
pip install -r requirements.txt

Dataset preparation

  • Download the splits prepared for the system from here that contains three subdirectories for 2020a, 2020b and 2021a respectively.
  • Unzip the file to data/.

Training and submitting

# for 2020a
python run.py --dataset_name 2020a --model_name bert-base-uncased

# or for 2020b
python run.py --edition 2020b --model_name bert-base-uncased
python run.py --edition 2020b --model_name google/electra-base-discriminator
python run.py --edition 2020b --model_name microsoft/deberta-base
python run.py --edition 2020b --model_name distilbert-base-uncased
python submit_ensemble.py --edition 2020b


# or for 2021a
python run.py --edition 2021a --model_name bert-base-uncased
python run.py --edition 2021a --model_name google/electra-base-discriminator
python run.py --edition 2021a --model_name microsoft/deberta-base
python run.py --edition 2021a --model_name distilbert-base-uncased
python submit_ensemble.py --edition 2021a

To see our results compared to other participating runs in 2020a and 2020b, check the appendix of this overview paper. To know the details of our approach, check this ISCRAM-2021 paper on 2020a and this TREC-2020 paper on 2020b. The evaluation for 2021a is still in process so the results will be added as soon as they come out.

Citation

If you use the code in your research, please consider citing the following papers:

@article{wang2021,
author = {Wang, Congcong and Nulty, Paul and Lillis, David},
journal = {Proceedings of the International ISCRAM Conference},
keywords = {18th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2021)},
number = {May},
title = {{Transformer-based Multi-task Learning for Disaster Tweet Categorisation}},
volume = {2021-May},
year = {2021}
}

@inproceedings{congcong2020multi,
 address = {Gaithersburg, MD},
 title = {Multi-task transfer learning for finding actionable information from crisis-related messages on social media},
 booktitle = {Proceedings of the Twenty-Nineth {{Text REtrieval Conference}} ({{TREC}} 2020)},
 author = {Wang, Congcong and Lillis, David},
 year = {2020},
}

Queries

Let me know if any questions via [email protected] or through creating an issue.

Owner
Congcong Wang
Ph.D [email protected], Crisis on Social Media, NLP, Machine Learning, IR
Congcong Wang
Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation

Shunted Transformer This is the offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation by Sucheng Ren, Daquan Zhou, Shengf

156 Dec 27, 2022
Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung

Vending_Machine_(Mesin_Penjual_Minuman) Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung Raw Sketch untuk Essay Ringkasan P

QueenLy 1 Nov 08, 2021
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Semi-supervised Deep Kernel Learning This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data

58 Oct 26, 2022
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023
Pytorch and Torch testing code of CartoonGAN

CartoonGAN-Test-Pytorch-Torch Pytorch and Torch testing code of CartoonGAN [Chen et al., CVPR18]. With the released pretrained models by the authors,

Yijun Li 642 Dec 27, 2022
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Zilong Huang 245 Dec 13, 2022
PyTorch implementation of residual gated graph ConvNets, ICLR’18

Residual Gated Graph ConvNets April 24, 2018 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbress

Xavier Bresson 112 Aug 10, 2022
Neural Radiance Fields Using PyTorch

This project is a PyTorch implementation of Neural Radiance Fields (NeRF) for reproduction of results whilst running at a faster speed.

Vedant Ghodke 1 Feb 11, 2022
Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a-Service". Being busy recently, the code in this repo and this tutoria

Tianxiang Sun 149 Jan 04, 2023
A Unified Generative Framework for Various NER Subtasks.

This is the code for ACL-ICJNLP2021 paper A Unified Generative Framework for Various NER Subtasks. Install the package in the requirements.txt, then u

177 Jan 05, 2023
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment

DK 20 Sep 09, 2022
Organseg dags - The repository contains the codebase for multi-organ segmentation with directed acyclic graphs (DAGs) in CT.

Organseg dags - The repository contains the codebase for multi-organ segmentation with directed acyclic graphs (DAGs) in CT.

yzf 1 Jun 12, 2022
Our CIKM21 Paper "Incorporating Query Reformulating Behavior into Web Search Evaluation"

Reformulation-Aware-Metrics Introduction This codebase contains source-code of the Python-based implementation of our CIKM 2021 paper. Chen, Jia, et a

xuanyuan14 5 Mar 05, 2022
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

195 Dec 07, 2022
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral) This is the official implementat

Yifan Zhang 259 Dec 25, 2022
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
Reinforcement Learning for Automated Trading

Reinforcement Learning for Automated Trading This thesis has been realized for the obtention of the Master's in Mathematical Engineering at the Polite

Pierpaolo Necchi 80 Jun 19, 2022
Rethinking Portrait Matting with Privacy Preserving

Rethinking Portrait Matting with Privacy Preserving This is the official repository of the paper Rethinking Portrait Matting with Privacy Preserving.

184 Jan 03, 2023