Dialogue Discourse Parsing as Generation: a Sequence-to-Sequence LLM-based Approach

This is the source code repository for the paper Dialogue Discourse Parsing as Generation: a Sequence-to-Sequence LLM-based Approach (SIGDial 2024).

Datasets

STAC

We used the linguistic-only STAC corpus and followed the separation of train, dev, test in Shi and Huang, A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues. In AAAI, 2019. The latest available verison on the website is available here. We share the dataset we used in data/stac/.

Molweni

Download from here. We use the original separation of train, dev, and test. Download the dataset and place it in data/molweni/.

How to run

Here is a step-by-step guide to fine-tune a T5 family model for discourse parsing:

Create a virtual environment

$ source virtualenvname/bin/activate
$ cd Seq2Seq-DDP/
$ pip install -r requirements.txt

Prepare structured data for fine-tuning

In dataprocess.py: process the original stac/molweni dataset and convert the raw text to structured text. Choose the structured text from: 'natural', 'augmented' (Seq2Seq-DDP) and 'focus', 'natural2' (Seq2Seq-DDP+transition). Examples for each structure type are given in data/stac_{structure}_train.json.

Fine-tuning

In train.py: give "do_train" as argument. This code fine-tunes a t5 familiy model for discourse parsing.

End2end prediction and transition-based prediction

• Seq2seq-DDP prediction: in train.py, give argument "do_test", choose structure type from 'augmented', 'natural'. Make sure to first put the fine-tuned model checkpoint in constant.py. Results will be written in generation/.

• Seq2Seq-DDP+transition system prediction: in transition_predict.py: choose structure type from 'focus', 'natural2'.

• evaluate.py: Evaluate predicted files in generation/ and calculate scores.

• constant.py: store paths, labels, etc.

Citation

Soon