NLU-Benchmark : Natural Language Understanding Benchmark

Korean(한국어)

NLU-Benchmark support natural language understanding experimental code for Close Domain chatbot or Task-Oriented artificial intelligence assistant system. Experiments provided by NLU-Benchmark include:

• Intent Classification : classify intent of the utterance or sentence
• Entity Recognition : extract entity information from utterance or sentence

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NLU-Benchmark is implemented based on Huggingface and PyTorch Lightning, and supports experiments with various language models.

Dependencies

• torch==1.9.0
• pytorch-lightning==1.4.1
• transformers>=4.7.0
• pandas
• sentencepiece
• scikit-learn
• seqeval

Usage

1. Download Dataset

NLU-Benchmark supports the following datasets.

dataset name	language	train	dev	test	link
ATIS	en	✔	✔	✔	LINK
SNIPS	en	✔	✔	✔	LINK

• After downloading datasets from above links, create a folder having each dataset name under the data folder and save downloaded datasets there, e.g., in case of ATIS dataset, save train, valid, test folders in data/atis.

• The following files are stored in the folders downloaded through above links.

  • seq.in : utterance sentences are stored.
  • label : intents about each sentences in seq.in are saved.
  • seq.out : sequence labels for each sentences in seq.in are saved. theses sequence labels follow BIO Format.

2. Pre-processing Dataset

To perform intent classification and entity recognition experiments via NLU-Benchmark, you should build label dictionaries about each dataset.

If you run the following command, intent.vocab. a dictionary about intent labels, and entity.vocab, a dictionary about entity labels, are created.

• --data_name : dataset name to create label dictionary, e.g., atis, snips

python data/build_label_vocabs.py --data_name {$DATA_NAME}

3. Intent Classification : Training and Evaluation

Run intent classification experiment by the following command:

• --model_type : type of model, e.g., bert, albert
• --model_name : checkpoint name of language model you wanna run experiment, e.g., bert-base-cased, albert-base-v2
• --data_name : dataset name to use training and evaluating, e.g., atis, snips
• --do_train : set training mode
• --do_eval : set evaluating mode
• --num_train_epochs : number of epochs in training
• --gpu_id : GPU id to be used when performing experiment
• --batch_size : batch size at training

python run_intent_classification.py --model_type {$MODEL_TYPE} \
                                    --model_name {$MODEL_NAME} \
                                    --data_name {$DATA_NAME} \
                                    --do_train \
                                    --do_eval \
                                    --num_train_epochs 20 \
                                    --gpu_id 0 \
                                    --batch_size 32

4. Entity Recognition : Training and Evaluation

Run entity recognition experiment by the following command:

• --model_type : type of model, e.g., bert, albert
• --model_name : checkpoint name of language model you wanna run experiment, e.g., bert-base-cased, albert-base-v2
• --data_name : dataset name to use training and evaluating, e.g., atis, snips
• --do_train : set training mode
• --do_eval : set evaluating mode
• --num_train_epochs : number of epochs in training
• --gpu_id : GPU id to be used when performing experiment
• --batch_size : batch size at training

python run_entity_recognition.py --model_type {$MODEL_TYPE} \
                                 --model_name {$MODEL_NAME} \
                                 --data_name {$DATA_NAME} \
                                 --do_train \
                                 --do_eval \
                                 --num_train_epochs 20 \
                                 --gpu_id 0 \
                                 --batch_size 32

Result

Hyper parameters for experiments are as follows:

Hyper Parameter	Value
max_seq_length	128
batch_size	32
learning_rate	5e-5
num_train_epochs	20

• If you look at the experimental results below, you can see that performance of the large size models have low performances. I think this problem can be solved by increasing the epoch and batch size.

1. ATIS Dataset : Intent Classification

Model Type	Model Name	Accuracy(%)
bert	bert-base-cased	97.31
	bert-base-uncased	96.84
	bert-large-cased	97.64
	bert-large-uncased	97.87
distilbert	distilbert-base-cased	96.97
	distilbert-base-uncased	97.31
roberta	roberta-base	95.74
	roberta-large	97.64
albert	albert-base-v2	97.08
	albert-large-v2	83.76
xlnet	xlnet-base-cased	97.76
	xlnet-large-cased	70.77
electra	google/electra-small-discriminator	88.91
	google/electra-base-discriminator	95.29
	google/electra-large-discriminator	70.77

2. ATIS Dataset : Entity Recognition

Model Type	Model Name	Precision(%)	Recall(%)	F1 Score(%)
bert	bert-base-cased	94.91	95.48	95.20
	bert-base-uncased	94.89	95.69	95.29
	bert-large-cased	95.36	95.73	95.54
	bert-large-uncased	95.53	95.80	95.67
distilbert	distilbert-base-cased	94.68	95.38	95.03
	distilbert-base-uncased	94.38	95.38	94.88
roberta	roberta-base	94.78	95.48	95.13
	roberta-large	94.99	95.62	95.31
albert	albert-base-v2	94.74	95.27	95.00
	albert-large-v2	94.24	95.24	94.74
xlnet	xlnet-base-cased	95.02	95.52	95.27
	xlnet-large-cased	94.86	95.13	95.00
electra	google/electra-small-discriminator	83.70	83.99	83.84
	google/electra-base-discriminator	94.72	95.62	95.17
	google/electra-large-discriminator	94.45	95.45	94.95

3. SNIPS Dataset : Intent Classification

Model Type	Model Name	Accuracy(%)
bert	bert-base-cased	97.14
	bert-base-uncased	97.71
	bert-large-cased	98.14
	bert-large-uncased	98.00
distilbert	distilbert-base-cased	98.14
	distilbert-base-uncased	97.57
roberta	roberta-base	97.71
	roberta-large	98.14
albert	albert-base-v2	98.00
	albert-large-v2	96.28
xlnet	xlnet-base-cased	97.14
	xlnet-large-cased	17.57
electra	google/electra-small-discriminator	97.57
	google/electra-base-discriminator	96.99
	google/electra-large-discriminator	11.42

4. SNIPS Dataset : Entity Recognition

Model Type	Model Name	Precision(%)	Recall(%)	F1 Score(%)
bert	bert-base-cased	93.27	94.58	93.92
	bert-base-uncased	94.32	95.58	94.95
	bert-large-cased	93.97	95.86	94.91
	bert-large-uncased	95.08	96.14	95.61
distilbert	distilbert-base-cased	93.74	95.41	94.57
	distilbert-base-uncased	93.59	95.47	94.52
roberta	roberta-base	90.63	93.01	91.81
	roberta-large	93.19	94.86	94.01
albert	albert-base-v2	93.57	95.25	94.40
	albert-large-v2	91.58	93.57	92.56
xlnet	xlnet-base-cased	93.74	95.41	94.57
	xlnet-large-cased	94.85	95.75	95.30
electra	google/electra-small-discriminator	92.31	94.58	93.43
	google/electra-base-discriminator	93.36	95.19	94.27
	google/electra-large-discriminator	92.83	94.86	93.83

TODO List

• README (Version. EN)
• add CRF layers

References

• yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification
• huggingface/transformers
• monologg/KoBERT-NER

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If you have any additional questions, please register an issue in this repository or contact sehunhu5247@gmail.com.