marcostoscano95 / Snippext_public

Snippext: Semi-supervised Opinion Mining with Augmented Data

Snippext

Snippext is the extraction pipeline for mining opinions and customer experiences from user-generated content (e.g., online reviews).

Paper: Zhengjie Miao, Yuliang Li, Xiaolan Wang, Wang-Chiew Tan, "Snippext: Semi-supervised Opinion Mining with Augmented Data", In theWebConf (WWW) 2020

Requirements

• Python 3.7.5
• PyTorch 1.3
• HuggingFace Transformers
• Spacy with the em_core_web_sm models
• NLTK (stopwords, wordnet)
• Gensim
• NVIDIA Apex (fp16 training)

Install required packages

conda install -c conda-forge nvidia-apex
pip install -r requirements.txt

Download pre-trained BERT models and word2vec models (for data augmentation) :

wget https://snippext.s3.us-east-2.amazonaws.com/finetuned_bert.zip
unzip finetuned_bert.zip
wget https://snippext.s3.us-east-2.amazonaws.com/word2vec.zip
unzip word2vec.zip

Training with the baseline BERT finetuning

The baseline method performs BERT finetuning on a specific task:

CUDA_VISIBLE_DEVICES=0 python train_baseline.py \
  --task restaurant_ae_tagging \
  --logdir results/ \
  --save_model \
  --finetuning \
  --batch_size 32 \
  --lr 5e-5 \
  --n_epochs 20 \
  --bert_path finetuned_bert/rest_model.bin

Parameters:

• --task: the name of the task (defined in configs.json)
• --logdir: the logging directory with Tensorboard
• --save_model: whether to save the best model
• --batch_size, --lr, --n_epochs: batch size, learning rate, and the number of epochs
• --bert_path (Optional): the path of a fine-tuned BERT checkpoint. Use the base uncased model if not specified.
• --max_len (Optional): maximum sequence length

(New) (also in MixDA and MixMatchNL):

• --fp16 (Optional): whether to train with fp16 acceleration
• --lm (Optional): other language models, e.g., "distilbert" or "albert"

Task Specification

The train/dev/test sets of a task (tagging or span classification) are specificed in the file configs.json. The file configs.json is a list of entries where each one is of the following format:

{
  "name": "hotel_tagging",
  "task_type": "tagging",
  "vocab": [
    "B-AS",
    "I-AS",
    "B-OP",
    "I-OP"
  ],
  "trainset": "combined_data/hotel/train.txt.combined",
  "validset": "combined_data/hotel/dev.txt",
  "testset": "combined_data/hotel/dev.txt",
  "unlabeled": "combined_data/hotel/unlabeled.txt"
},

Fields:

• name: the name of the task. A tagging task should end with a suffix _tagging
• task_type: either tagging or classification
• vocab: the list of class labels. For tagging tasks, all labels start with B- or I- indicating the begin/end of a span. For classification task, the list contains all the possible class labels.
• trainset, validset, testset: the paths to the train/dev/test sets
• unlabeled (Optional): the path to the unlabeled dataset for semi-supervised learning. The file has same format as the train/test sets but the labels are simply ignored.

Training with MixDA (data augmentation)

1. Build the augmentation index:

python augment_index_builder.py \
  --task restaurant_ae_tagging \
  --w2v_path word2vec/rest_w2v.model \
  --idf_path word2vec/rest_finetune.txt \
  --bert_path finetuned_bert/rest_model.bin \
  --index_output_path augment/rest_index.json

Simply replace restaurant_ae_tagging with laptop_ae_tagging, restaurant_asc, and laptop_asc to generate the other indices. Replace rest with laptop for the laptop_ae_tagging and laptop_asc indices.

2. Train with:

CUDA_VISIBLE_DEVICES=0 python train_mixda.py \
  --task restaurant_ae_tagging \
  --logdir results/ \
  --finetuning \
  --batch_size 32 \
  --lr 5e-5 \
  --n_epochs 5 \
  --bert_path finetuned_bert/rest_model.bin \
  --alpha_aug 0.8 \
  --augment_index augment/rest_index.json \
  --augment_op token_repl_tfidf \
  --run_id 0 

Parameters:

• alpha_aug: the mixup parameter between the original example and the augmented example ({0.2, 0.5, 0.8} are usually good).
• augment_index: the path to the augmentation indices
• augment_op: the name of the DA operators. We currently support the following 9 operators:

Operators	Details
token_del_tfidf	Token deletion by importance (measured by TF-IDF)
token_del	Token deletion (uniform)
token_repl_tfidf	Token replacement by importance
token_repl	Token replacement (uniform)
token_swap	Swapping two tokens
token_ins	Inserting new tokens
span_sim	Replacing a span with similar a one
span_freq	Replacing a span by frequency
span	Uniform span replacement

Training with MixMatchNL (MixDA + Semi-supervised Learning)

Our implementation of MixMatch with MixDA. To train with MixMatchNL:

CUDA_VISIBLE_DEVICES=0 python train_mixmatchnl.py \
  --task restaurant_ae_tagging \
  --logdir results/ \
  --finetuning \
  --batch_size 32 \
  --lr 5e-5 \
  --n_epochs 5 \
  --bert_path finetuned_bert/rest_model.bin \
  --alpha_aug 0.8 \
  --alpha 0.2 \
  --u_lambda 50.0 \
  --num_aug 2 \
  --augment_index augment/rest_index.json \
  --augment_op token_repl_tfidf \
  --run_id 0 

Additional parameters:

• alpha: the mixup parameter (between labeled and unlabeled data). We chose alpha from {0.2, 0.5, 0.8}.
• u_lambda: the weight of unlabeled data loss, typically (chosen from {10.0, 25.0, 50.0})
• num_aug: the number of augmented examples per unlabeled example (we chose from {2, 4})

Hyperparameters and experiment scipts (coming soon)