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SemBERT: Semantics-aware BERT for Language Understanding

(2020/10/07) Update: Tips for possible issues

1. SRL prediction mismatches the provided samples

The POS tags are slightly different using different spaCy versions. SemBERT used spacy==2.0.18 to obtain the verbs.

Refer to allenai/allennlp#3418, cooelf/SemBERT#12 (CHN).

2. SRL is not a registered name for Model.

Please try pip install --pre allennlp-models

3. Issues about AllenNLP

If you encounter issues about the class or variables in AllenNLP, please try to use a lower version, e.g., 0.8.1.

Our experiment environment for reference:

Python 3.6+ PyTorch (1.0.0) AllenNLP (0.8.1)

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Codes for the paper Semantics-aware BERT for Language Understanding in AAAI 2020

Overview

Requirements

(Our experiment environment for reference)

Python 3.6+ PyTorch (1.0.0) AllenNLP (0.8.1)

Datasets

GLUE data can be downloaded from GLUE data by running this script and unpack it to directory <u>glue_data</u>. We provide an example data sample in <u>glue_data/MNLI</u> to show how SemBERT works.

Instructions

This repo shows the example implementation of SemBERT for NLU tasks. We basically used the pre-trained BERT uncased models so do not forget to pass the parameter --do_lower_case.

The example script are as follows:

Train a model

Note: please replace the sample data with labeled data (use our labeled data or annotate your data following the instructions below).

CUDA_VISIBLE_DEVICES=0 \
python run_classifier.py \
--data_dir glue_data/SNLI/ \
--task_name snli \
--train_batch_size 32 \
--max_seq_length 128 \
--bert_model bert-wwm-uncased \
--learning_rate 2e-5 \
--num_train_epochs 2 \
--do_train \
--do_eval \
--do_lower_case \
--max_num_aspect 3 \
--output_dir glue/snli_model_dir

Evaluation

Both run_classifier.py and run_snli_predict.py can be used for evaluation, where the later is simplified for easy employment.

The major difference is that run_classifier.py takes labeled data as input, while run_snli_predict.py integrates the real-time semantic role labeling, so it uses the original raw data.

Evaluation using labeled data

CUDA_VISIBLE_DEVICES=0 \
python run_classifier.py \
--data_dir glue_data/SNLI/ \
--task_name snli \
--eval_batch_size 128 \
--max_seq_length 128 \
--bert_model bert-wwm-uncased \
--do_eval \
--do_lower_case \
--max_num_aspect 3 \
--output_dir glue/snli_model_dir

Evaluation using raw data (with real-time semantic role labeling)

Our trained SNLI model (reaching 91.9% test accuracy) can be accessed here.

https://drive.google.com/drive/folders/1Yn-WCw1RaMxbDDNZRnoJCIGxMSAOu20_?usp=sharing

To use our trained SNLI model, please put the SNLI model and the SRL model to the snli_model_dir and srl_model_dir, respectively.

As shown in our example SNLI model, the folder of snli_model_dir should contain three files:

vocab.txt and bert_config.json from the BERT model folder that are used for training your model;

pytorch_model.bin that is the trained SNLI model.

CUDA_VISIBLE_DEVICES=0 \
python run_snli_predict.py \
--data_dir /share03/zhangzs/glue_data/SNLI \
--task_name snli \
--eval_batch_size 128 \
--max_seq_length 128 \
--max_num_aspect 3 \
--do_eval \
--do_lower_case \
--bert_model snli_model_dir \
--output_dir snli_model_dir \
--tagger_path srl_model_dir

For prediction, use the flag: --do_predict for either the script run_classifier.py or run_snli_predict.py. The output pred file can be directly used for GLUE online submission and evaluation.

Data annotation (Semantic role labeling)

We provide two kinds of semantic labeling method,

• online: each word sequence are passed to label module to obtain the tags which could be used for online prediction. This would be time-consuming for large corpus. See tag_model/tagging.py

  If you want to use the online one, please specify the --tagger_path parameter in the run.py file.

• offline: the current one that pre-process the datasets and save them for later loading for training and evaluation. See tag_model/tagger_offline.py

  Our labeled data can be downloaded here for quick start.

  Google Drive: https://drive.google.com/file/d/1B-_IRWRvR67eLdvT6bM0b2OiyvySkO-x/view?usp=sharing

  Baidu Cloud:

  Link https://pan.baidu.com/s/1EduMJAfEXet_9yCfVob9qA Password：sl7l

Note this repo is based on the offline version, so that the column id/index in the data-processor would be slightly different from the original, which is like this:

text_a = line[-3] text_b = line[-2] label = line[-1]

If you use the original data <u>instead of</u> our preprocessed one by tag_model/tagger_offline.py, please modify the index according to the dataset structure.

SRL model

The SRL model in this implementation used the ELMo-based SRL model from AllenNLP.

Recently, there is a new BERT-based model, which is a nice alternative.

Reference

Please kindly cite this paper in your publications if it helps your research:

@inproceedings{zhang2020SemBERT,
	title={Semantics-aware {BERT} for language understanding},
	author={Zhang, Zhuosheng and Wu, Yuwei and Zhao, Hai and Li, Zuchao and Zhang, Shuailiang and Zhou, Xi and Zhou, Xiang},
  	booktitle={the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)},
	year={2020}
}