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[NAACL 2022] TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding

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# TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding (NAACL 2022)

Pytorch Implementation of [TreeMix](https://arxiv.org/abs/2205.06153)

![treemix_example](https://i.loli.net/2021/11/30/EhX9iZax3s6jpTC.jpg)

## Abstract

Data augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word replacement, back-translation etc. Though effective, they missed one important characteristic of language–compositionality, meaning of a complex expression is built from its subparts. Motivated by this, we propose a compositional data augmentation approach for natural language understanding called TreeMix. Specifically, TreeMix leverages constituency parsing tree to decompose sentences into constituent sub-structures and the Mixup data augmentation technique to recombine them to generate new sentences. Compared with previous approaches, TreeMix introduces greater diversity to the samples generated and encourages models to learn compositionality of NLP data. Extensive experiments on text classification and semantic parsing benchmarks demonstrate that TreeMix outperforms current stateof-the-art data augmentation methods.

## Code Structure

```
|__ DATA
	|__ SST2 
		|__ data
			|__ train.csv --> raw train dataset
			|__ test.csv --> raw test dataset
			|__ train_parsing --> consituency parsing results
		|__ generated 
			|__ times2_min0_seed0_0.3_0.1_7k --> augmentation dataset(hugging face dataset format) with 2 times bigger, seed 0, lambda_L=0.1, lambda_U=0.3, total size=7k
		|__ logs --> best results log
		|__ runs --> tensorboard results
			|__ aug --> augmentation only baseline
			|__ raw --> standard baseline
			|__ raw_aug --> TreeMix results
|__ checkpoints 
	|__ best.pt --> Best model checkpoints
|__ process_data / --> Download & Semantic parsing using Stanfordcorenlp tooktiks
	|__ Load_data.py --> Loading raw dataset and augmentation dataset
	|__ get_data.py --> Download all dataset from huggingface dataset and perform constituency parsing to obtain processed dataset
	|__ settings.py --> Hyperparameter settings & Task settings
|__ online_augmentation 
	|__ __init__.py --> Random Mixup 
|__ Augmentation.py --> Subtree Exchange augmentation method based on consituency parsing results for all dataset(single sentence classification, sentence relation classification, SCAN dataset)
|__ run.py --> Train for one dataset
|__ batch_train.py -> Train with different datasets and different settings, specified by giving specific arguments 
```

### Getting Started

```
pip install -r requirements.txt
```

Note that to successfully run TreeMix, you must install `stanfordcorenlp`. Please refer to this

[corenlp]( https://stanfordnlp.github.io/CoreNLP/download.html "stanfordcorenlp") for more information.

### Download & Constituency Parsing

```
cd process_data
python get_data.py --data {DATA} --corenlp_dir {CORENLP}
```

`DATA` indicates the dataset name, `CORENLP` indicates the directory of `stanfordcorenlp` . After this process, you could get corresponding data folder in `DATA/` and `train_parsing.csv`.

### TreeMix Augmentation

```
python Augmentation.py --data {DATASET} --times {TIMES} --lam1 {LAM1} --lam2 {LAM2} --seeds {SEEDS} 
```

Augmentation with different arguments, will generate #(TIMES)×#(SEEDS) extra dataset. `DATASET` could be **list of data name** such as 'sst2 rte', since 'trec' has two versions, you need to input `--label_name {}`  to specify whether the trec-fine or trec-coarse set. Besides, by typing `--low_resource` , it will generated partial augmentation dataset as well as partial train set. You can modify the hyperparameter `lambda_U` and `lambda_L` by changing `LAM1` and `LAM2` . `TIMES` could be **a list of intergers** such as 2,5 to assign the size of the augmentation dataset.

### Model Training

```
python batch_train.py --mode {MODE} --data {DATASET}
```

Evaluation one dataset for all its augmenation set with specific mode. `MODE` can be 'raw', 'aug', 'raw_aug', which indicates train the model with raw dataset only, augmentation dataset only and combination of raw and augmentation set respectively. `DATASET` should be **one specific dataset** name. This will report all results of a specifc dataset in `/log`. If not specified, the hyperparameters will be set as in `/process_data/settings.py`, please look into this file for more arguments information.

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