https://github.com/grig-guz/tree-content-structuring
Content structuring for NLG with discourse dependency trees.
https://github.com/grig-guz/tree-content-structuring
content-structuring nlg nlg-dataset rst
Last synced: 3 months ago
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Content structuring for NLG with discourse dependency trees.
- Host: GitHub
- URL: https://github.com/grig-guz/tree-content-structuring
- Owner: grig-guz
- Created: 2020-11-30T00:14:35.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-11-30T00:18:04.000Z (over 4 years ago)
- Last Synced: 2025-01-20T10:34:22.818Z (5 months ago)
- Topics: content-structuring, nlg, nlg-dataset, rst
- Language: Python
- Homepage:
- Size: 37.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Domain-Independent Neural Text Structuring
This is the code for our [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.281/) on text structuring with silver-standard discourse trees from [MEGA-DT treebank](https://www.cs.ubc.ca/cs-research/lci/research-groups/natural-language-processing/mega_dt.html).
## Requirements
* Python (3.6+)
* [Pytorch](https://pytorch.org/) (1.3.0+)
* [dgl](https://www.dgl.ai/) (0.4.2 strictly)
* [Transformers](https://huggingface.co/transformers/) (3.0.2)
## Running experiments
1. Create the folder named "data".
2. Download the pickled versions of MEGA-DT [here](https://www.todo) (100k train, 250k train, 5k val, 15k test), and place it in the "data" folder.
3. Run the train/testing script as described below. Each scripts accepts a single numeric (1 or 2) indicating whether the model should be trained on 100k or 250k version of MEGA-DT.
#### Dependency Model
To train/evaluate the dependency model,
```bash
bash scripts/train_dep.sh dataset_id
bash scripts/eval_dep.sh dataset_id
```
#### Pointer Model
```bash
bash scripts/train_pointer.sh dataset_id
bash scripts/eval_pointer.sh dataset_id
```
#### Dependency no-pointer Baseline
```bash
bash scripts/train_dep_treetrain_baseline.sh dataset_id
bash scripts/eval_dep_treetrain_baseline.sh dataset_id
```
#### Language Model Decoding Baseline
```bash
bash scripts/eval_lm_baseline.sh
```
## Configuration
You can set hyperparameters and device type in the training/testing scripts for each model individually. The parameter values used in our experiments are already specified there.
## Citation
```
@inproceedings{guz-carenini-2020-towards,
title = "Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks",
author = "Guz, Grigorii and
Carenini, Giuseppe",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.281",
pages = "3141--3152",
}
```