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--- |
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language: |
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- en |
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tags: |
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- formality |
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licenses: |
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- cc-by-nc-sa |
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license: cc-by-nc-sa-4.0 |
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--- |
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**Model Overview** |
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This is the model presented in the paper "Detecting Text Formality: A Study of Text Classification Approaches". |
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The original model is [DeBERTa (large)](https://huggingface.co/microsoft/deberta-v3-large). Then, it was fine-tuned on the English corpus for fomality classiication [GYAFC](https://arxiv.org/abs/1803.06535). |
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In our experiments, the model showed the best results within Transformer-based models for the task. More details, code and data can be found [here](https://github.com/s-nlp/formality). |
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**Evaluation Results** |
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Here, we provide several metrics of the best models from each category participated in the comparison to understand the ranks of values. This is the task of English monolingual formality classification. |
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| | acc | f1-formal | f1-informal | |
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|------------------|------|-----------|-------------| |
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| bag-of-words | 79.1 | 81.8 | 75.6 | |
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| CharBiLSTM | 87.0 | 89.0 | 84.0 | |
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| DistilBERT-cased | 80.1 | 83.0 | 75.6 | |
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| DeBERTa-large | 87.8 | 89.0 | 86.1 | |
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**How to use** |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_name = 'deberta-large-formality-ranker' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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``` |
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**Citation** |
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``` |
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@inproceedings{dementieva-etal-2023-detecting, |
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title = "Detecting Text Formality: A Study of Text Classification Approaches", |
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author = "Dementieva, Daryna and |
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Babakov, Nikolay and |
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Panchenko, Alexander", |
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editor = "Mitkov, Ruslan and |
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Angelova, Galia", |
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booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing", |
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month = sep, |
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year = "2023", |
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address = "Varna, Bulgaria", |
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publisher = "INCOMA Ltd., Shoumen, Bulgaria", |
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url = "https://aclanthology.org/2023.ranlp-1.31", |
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pages = "274--284", |
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abstract = "Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation{---}GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments {--} monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.", |
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} |
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``` |
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## Licensing Information |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |