Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention, ICLR 2023, Tiny Papers
https://arxiv.org/abs/2305.00262
This is the official code repository of our ICLR 2023 Tiny paper. In this paper, we proposed a simple but effective Hierarchical Dialogue Understanding model, HiDialog. we first insert multiple special tokens into a dialogue and propose the turn-level attention to learn turn embeddings hierarchically. Then, a heterogeneous graph module is leveraged to polish the learned embeddings.
Our experiments are conducted with following core packages:
- PyTorch 1.11.0
- CUDA 11.6
- dgl-cuda11.3 0.8.2
- sklearn
Main Results
Reproducibility
To reproduce our training process in main experiments on DialogRE,
- download RoBERTa and unzip it to
pre-trained_model/RoBERTa/
. - download
config.json
,merges.txt
andvocab.json
from here, put them topre-trained_model/RoBERTa/
- download DialogRE
- copy the *.json files into datasets/DialogRE
- run
bash dialogre.sh
To reproduce our training process in main experiments on MELD,
- download RoBERTa and unzip it to
pre-trained_model/RoBERTa/
. - download
config.json
,merges.txt
andvocab.json
from here, put them topre-trained_model/RoBERTa/
- download MELD
- copy the *.json files into datasets/MELD
- run
python MELD.py
- run
bash meld.sh
This project is expanded upon from a course project at NUS [Course Page]. The code repository is based on following projects:
- ACL-20, "Dialogue-Based Relation Extraction" [github]
- AAAI-21, "GDPNet: Refining Refining Latent Multi-View Graph for Relation Extraction" [github]
- EMNLP-21, "Graph Based Network with Contextualized Representations of Turns in Dialogue" [github]
Thanks for their amazing work.
@Article{liu2023hierarchical,
author = {Xiao Liu and Jian Zhang and Heng Zhang and Fuzhao Xue and Yang You},
title = {Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention},
journal = {arXiv preprint arXiv:2305.00262},
year = {2023},
}