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Chinese-Idiom-Paraphrasing
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Chinese-Idiom-Paraphrasing
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## Chinese Idiom Paraphrasing Chinese Idiom Paraphrasing (CIP), which goal is to rephrase the idioms of input sentence to generate a fluent, meaning-preserving sentence without any idiom:  Data in this [dataset](./data) and several approaches: - LSTM approach - Transformer approach - mt5 approach - infill approach ### Dependecies - Python>=3.6 - torch>=1.7.1 - transformers==4.8.0 - fairseq==0.10.2 ### Pre-trained model you can download all pre-trained models [here](https://pan.baidu.com/s/1zYffBCf7zAFXNhBraJXqOw?pwd=4s9n)(4s9n), and put it into```model```directory. If you want train models from scratch, you need uses the pre-trained language models t5-pegasus ([ZhuiyiTechnology](https://github.com/ZhuiyiTechnology/t5-pegasus)) and place the models under the ```model``` directory after downloading. ### Train train LSTM and Transformer model by fairseq, you need process data for jieba and bpe tokenize sentence, we use scripts from Subword-nmt: ```shell git clone https://github.com/rsennrich/subword-nmt ``` Then run ```shell sh prepare.sh ``` train LSTM, Transformer, t5-pegasus, infill model ```shell sh train_lstm.sh sh train_transformer.sh sh train_t5_pegasus.sh sh train_infill.sh ``` ### Generate Run the following command to generate ```shell sh fairseq_generate.sh sh generate_t5_pegasus.sh sh generate_infill.sh ``` ## Citation ``` @article{qiang2022chinese, title={Chinese Idiom Paraphrasing}, author={Qiang, Jipeng and Li, Yang and Zhang, Chaowei and Li, Yun and Yuan, Yunhao and Zhu, Yi and Wu, Xindong}, journal={arXiv preprint arXiv:2204.07555}, year={2022} } ```
