achen353 / dacon

Data Augmentation for Entity Matching using Consistency Learning

DACon

Code for CS 8803 DMM Project: Data Augmentation for Entity Matching using Consistency Learning

Requirements

• Python 3.7+
• PyTorch 1.10.0+cu111: default CUDA version 11.1 (change the --find-links in requirements.txt for other versions)
• Transformers 4.12.3
• NVIDIA Apex (fp16 training): requires Nvidia graphic card

Install required packages

pip install -r requirements.txt
# Apex requires CUDA-supported graphic cards (Nvidia graphic cards)
git clone https://github.com/NVIDIA/apex.git
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Experiment Results

F1 scores on all EM datasets

Method	Abt-Buy	Amazon-Google	DBLP-ACM (clean/dirty)	DBLP-Scholar (clean/dirty)	Walmart-Amazon (clean/dirty)
DM + RoBERTa	85.71	82.35	97.92/97.92	92.63/91.49	72.73/67.92
RoBERTa	80.49	65.02	98.97/96.63	92.18/92.30	71.50/73.55
InvDA	84.26	59.70	96.97/96.51	91.99/91.69	71.85/73.48
Rotom	76.34	62.38	96.76/97.16	91.80/91.63	66.28/76.66
Rotom + SSL	81.89	62.34	98.09/97.20	92.88/92.97	72.19/71.55
DACon Baseline	81.06	62.95	96.73/96.98	92.42/92.00	73.71/71.91
DACon One-to-Many	80.21	61.06	96.96/97.19	91.94/91.45	76.88/75.14
DACon Fixed Consistency	83.60	62.87	92.29/96.40	91.33/91.69	78.93/74.18
DACon Consistency	80.81	60.30	97.29/95.95	92.02/91.86	74.60/72.11

Average training time with different training + validation size

Method	300	450	600	780
DM + RoBERTa	65.17	90.46	114.28	139.11
RoBERTa	100.67	111.24	121.17	132.65
InvDA	112.45	130.90	148.07	166.12
Rotom	165.73	216.40	263.81	313.18
Rotom + SSL	165.73	216.17	260.64	313.51
DACon Baseline	168.78	182.93	196.23	211.42
DACon One-to-Many	190.14	216.42	238.87	264.89
DACon Fixed Consistency	190.93	216.88	240.85	266.48
DACon Consistency	185.49	218.16	240.49	266.91

See the report for full results and experiment details.

Model Training

To train a model with Rotom:

CUDA_VISIBLE_DEVICES=0 python train_any.py \
  --task em_DBLP-ACM \
  --size 300 \
  --logdir results_em/ \
  --finetuning \
  --batch_size 32 \
  --lr 3e-5 \
  --n_epochs 20 \
  --max_len 128 \
  --fp16 \
  --lm roberta \
  --da dacon_baseline \
  --balance \
  --run_id 0

The current version supports 3 tasks: entity matching (EM), error detection (EDT), and text classification (TextCLS). The supported tasks are:

Type	Dataset Names	taskname pattern
EM	Abt-Buy, Amazon-Google, DBLP-ACM, DBLP-GoogleScholar, Walmart-Amazon	em_{dataset}, e.g., em_DBLP-ACM
EDT	beers, hospital, movies, rayyan, tax	cleaning_{dataset}, e.g., cleaning_beers
TextCLS	AG, AMAZON2, AMAZON5, ATIS, IMDB, SNIPS, SST-2, SST-5, TREC	textcls_{dataset}, e.g., textcls_AG
TextCLS, splits from Hu et al.	IMDB, SST-5, TREC	compare1_{dataset}, e.g., compare1_IMDB
TextCLS, splits from Kumar et al.	ATIS, SST-2, TREC	compare2_{dataset}, e.g., compare2_ATIS

Parameters:

• --task: the taskname pattern specified following the above table
• --size: the dataset size (optional). If not specified, the entire dataset will be used. The size ranges are {300, 450, 600, 750} for EM, {50, 100, 150, 200} For EDT, and {100, 300, 500} for TextCLS
• --logdir: the path for TensorBoard logging (F1, acc, precision, and recall)
• --finetuning: always keep this flag on
• --batch_size, --lr, --max_len, --n_epochs: the batch size, learning rate, max sequence length, and the number of epochs for model training
• --fp16: whether to use half-precision for training
• --lm: the language model to fine-tune. We currently support bert, distilbert, and roberta
• --balance: a special option for binary classification (EM and EDT) with skewed labels (#positive labels >> #negative labels). If this flag is on, then the training process will up-sample the positive labels
• --warmup: (new) if this flag is on with SSL, then first warm up the model by training it on labeled data only before running SSL. Only support EM for now.
• --run_id: the integer ID of the run e.g., {0, 1, 2, ...}
• --da: the data augmentation method (See table below)

Method	Operator Name(s)
No DA (simply LM fine-tuning)	None
Regular transformation-based DA	['del', 'drop_col', 'append_col', 'swap', 'ins'] for EM/EDT ['del', 'token_del_tfidf', 'token_del', 'shuffle', 'token_repl', 'token_repl_tfidf'] for TextCLS
Inversed DA (InvDA)	t5 / invda
Rotom (w/o semi-supervised learning)	auto_filter_weight_no_ssl
Rotom (w. semi-supervised learning)	auto_filter_weight
DACon Baseline	dacon_baseline
DACon One-to-Many	dacon_one_to_many
DACon Fixed Consistency	dacon_fixed_consistency
DACon Consistency	dacon_consistency

For the invda fine-tuning, see invda/README.md.

Experiment scripts

All experiment scripts are available in scripts/. To run the experiments for a task (em, cleaning, or textcls):

python scripts/run_all_em.py