This repository contains the PyTorch source Code for our paper: DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition.
Bowen Xing and Ivor W. Tsang.
ACL 2022 (Findings).
DARER's Architecture:
Our code relies on Python 3.6 and following libraries:
- transformers==1.1.0
- torch-geometric==1.7.0
- torch==1.5.0
- tqdm==4.60.0
- transformers==3.3.1
- numpy==1.19.2
- scikit-learn==0.24.2
LSTM-based Encoder:
DARER/
# Mastodon //glove
python -u main.py -lr 1e-3 -l2 1e-8 -dd dataset/mastodon -hd 128 -mc 2 -dr 0.2 -sn 3
# DailyDialog // glove
python -u main.py -ne 50 -hd 300 -lr 1e-3 -l2 1e-8 -dd dataset/dailydialogue -rnb 10 -sn 2 -mc 5 -dr 0.5
# DailyDialog // train random word vector
python -u main.py -ne 50 -hd 256 -lr 1e-3 -l2 1e-8 -dd dataset/dailydialogue -sn 1 -mc 1e-05 -dr 0.3 -rw
PTLM(pre-trained language model)-based Encoder:
DARER/pre-trained language model/
# Mastodon // BERT
python -u main.py -pm bert -bs 16 -sn 4 -dr 0.3 -hd 768 -l2 0.01 -blr 1e-05 -mc 1
# Mastodon // RoBERTa
python -u main.py -pm roberta -bs 16 -sn 4 -dr 0.14 -hd 768 -l2 0.0 -blr 1e-05 -mc 1
# Mastodon // XLNet
python -u main.py -pm xlnet -bs 12 -sn 4 -dr 0.2 -hd 256 -l2 0.0 -blr 1e-05 -mc 1We recommend you search the optimal hyper-parameters on your server to obtain the best performances in your own experiment environment.
If the code is used in your research, please star this repo ^_^ and cite our paper as follows:
@inproceedings{xing-tsang-2022-darer,
title = "{DARER}: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition",
author = "Xing, Bowen and
Tsang, Ivor",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.286",
doi = "10.18653/v1/2022.findings-acl.286",
pages = "3611--3621",
}
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