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.

You might also like...
Repository for
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Multi-task yolov5 with detection and segmentation based on yolov5
Multi-task yolov5 with detection and segmentation based on yolov5

YOLOv5DS Multi-task yolov5 with detection and segmentation based on yolov5(branch v6.0) decoupled head anchor free segmentation head README中文 Ablation

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods."

pv_predict_unet-lstm Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods." IEEE Transactions

Implementation of PyTorch-based multi-task pre-trained models

mtdp Library containing implementation related to the research paper "Multi-task pre-training of deep neural networks for digital pathology" (Mormont

A multi-entity Transformer for multi-agent spatiotemporal modeling.
A multi-entity Transformer for multi-agent spatiotemporal modeling.

baller2vec This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotempor

A list of multi-task learning papers and projects.

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey paper.

A list of multi-task learning papers and projects.

A list of multi-task learning papers and projects.

Owner
Congcong Wang
Ph.D student@UCD, Crisis on Social Media, NLP, Machine Learning, IR
Congcong Wang
Predicting Tweet Sentiment Maching Learning and streamlit

Predicting-Tweet-Sentiment-Maching-Learning-and-streamlit (I prefere using Visual Studio Code ) Open the folder in VS Code Run the first cell in requi

null 1 Nov 20, 2021
Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders

MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo

Visual Intelligence & Learning Lab, Swiss Federal Institute of Technology (EPFL) 385 Jan 6, 2023
Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling

TGraM Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling, Qibin He, Xian Sun, Zhiyuan Yan, Beibei Li, Kun Fu Abstract Rece

Qibin He 6 Nov 25, 2022
Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation", Haoxiang Wang, Han Zhao, Bo Li.

Bridging Multi-Task Learning and Meta-Learning Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Trainin

AI Secure 57 Dec 15, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 5, 2022
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
Alex Pashevich 62 Dec 24, 2022
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

Jiezhang Cao 225 Nov 13, 2022
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

null 94 Dec 22, 2022