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--- |
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language: |
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- en |
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--- |
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# ConPGS Model |
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**Con**trollable **P**araphrase **G**eneration for Semantic and Lexical **S**imilarities Model |
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Github: https://github.com/Ogamon958/ConPGS |
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## Model Description |
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ConPGS Model is capable of generating paraphrases while controlling for semantic and lexical similarity. |
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The semantic similarity can be controlled at six levels - 70, 75, 80, 85, 90 and 95 - the higher the level, the more similar the model outputs sentences with similar meanings. |
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The lexical similarity can be controlled in eight steps 5, 10, 15, 20, 25, 30, 35 and 40, the higher the level, the more the model outputs sentences with similar surfaces. |
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## How to use this model |
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``` |
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#setup |
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import torch |
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from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig |
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model = BartForConditionalGeneration.from_pretrained('Ogamon/conpgs_model') |
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tokenizer = BartTokenizer.from_pretrained('Ogamon/conpgs_model') |
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device= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
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model.to(device) |
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#tags |
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sim_token = {70:"<SIM70>", 75:"<SIM75>",80:"<SIM80>",85:"<SIM85>",90:"<SIM90>",95:"<SIM95>"} |
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bleu_token={5:"<BLEU0_5>",10:"<BLEU10>",15:"<BLEU15>",20:"<BLEU20>",25:"<BLEU25>",30:"<BLEU30>",35:"<BLEU35>",40:"<BLEU40>"} |
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``` |
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``` |
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#edit here |
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text = "The tiger sanctuary has been told their 147 cats must be handed over." |
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sim = sim_token[95] #70,75,80,85,90,95 |
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bleu = bleu_token[5] #5,10,15,20,25,30,35,40 |
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#evaluate |
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model.eval() |
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with torch.no_grad(): |
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input_text=f"{sim} {bleu} {text}" |
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inputs = tokenizer.encode(input_text, return_tensors="pt",truncation=True).to(device) |
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length=inputs.size()[1] |
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max_len=int(length*1.5) |
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min_len=int(length*0.75) |
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summary_ids = model.generate(inputs,max_length=max_len,min_length=min_len,num_beams=5) |
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summary = tokenizer.decode(summary_ids[0],skip_special_tokens=True) |
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print(summary) |
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#The tiger sanctuary was told to hand over its 147 cats. |
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``` |
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## Citation |
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Please cite our LREC-COLING2024 paper if you use this repository: |
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(Details updated after submission.) |
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``` |
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@inproceedings{ogasa-2024-lrec-coling, |
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title = {{Controllable Paraphrase Generation for Semantic and Lexical Similarities}}, |
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author = "Ogasa, Yuya and Kajiwara, Tomoyuki and Arase, Yuki", |
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booktitle = "The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation", |
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month = may, |
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year = "2024", |
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address = "Torino, Italia", |
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publisher = "Association for Computational Linguistics", |
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abstract = "We developed a controllable paraphrase generation model for semantic and lexical similarities using a simple and intuitive mechanism: attaching tags to specify these values at the head of the input sentence. Lexically diverse paraphrases have been long coveted for data augmentation. However, their generation is not straightforward because diversifying surfaces easily degrades semantic similarity. Furthermore, our experiments revealed two critical features in data augmentation by paraphrasing: appropriate similarities of paraphrases are highly downstream task-dependent, and mixing paraphrases of various similarities negatively affects the downstream tasks. These features indicated that the controllability in paraphrase generation is crucial for successful data augmentation. We tackled these challenges by fine-tuning a pre-trained sequence-to-sequence model employing tags that indicate the semantic and lexical similarities of synthetic paraphrases selected carefully based on the similarities. The resultant model could paraphrase an input sentence according to the tags specified. Extensive experiments on data augmentation for contrastive learning and pre-fine-tuning of pretrained masked language models confirmed the effectiveness of the proposed model. We release our paraphrase generation model and a corpus of 87 million diverse paraphrases. (https://github.com/Ogamon958/ConPGS)" |
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} |
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``` |
