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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# AffilGood-AffilRoBERTa |
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For the first two tasks, we fine-tuned two [RoBERTa](https://huggingface.co/docs/transformers/en/model_doc/roberta) and [XLM-RoBERTa](https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta) |
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models for (predominantly) English and multilingual datasets, respectively. [Gururangan *et al.* (2020)](https://aclanthology.org/2020.acl-main.740.pdf) show that |
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continuing pre-training language models on task-relevant unlabeled data might contribute to improve the performance of final fine-tuned task-specific |
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models-in particular, in low-resource situations. Considering the fact that the affiliation strings' *grammar* has its own structure, |
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which is different from the one that would be expected to be found in free natural language, we explore whether our affiliation span identification and |
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NER models would benefit from being fine-tuned from models that have been *further pre-trained* on raw affiliation strings for the masked token prediction task. |
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We adatap RoBERTa-ase to 10 million random raw affiliation strings from OpenAlex, reporting perplexity on 50k randomly held-out affiliation strings. |
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In what follows, we refer to our adapted models as AffilRoBERTa (adapted RoBERTa model) and AffilXLM (adapted XLM-RoBERTa). |
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Specific details of the adaptive pre-training procedure can be found in [Duran-Silva *et al.* (2024)](https://aclanthology.org/2024.sdp-1.13.pdf). |
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## Evaluation |
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We report masked language modeling loss as perplexity measure (PPL) on 50k randomly sampled held-out raw affiliation strings. |
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| **Model** | PPL<sub>base</sub> | PPL<sub>adapt</sub> | |
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|-----------------|--------------------|----------------------| |
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| RoBERTa | 1.972 | 1.106 | |
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| XLM-RoBERTa | 1.997 | 1.101 | |
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AffilGood-AffilRoBERTa achieves competitive performance to 2 tasks in processing affiliation strings, compared to base models |
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| Task| RoBERTa | XLM | **AffilRoBERTa (this model)** | AffilXLM | |
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|-----|------|------|------|----------| |
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| AffilGood-NER | .910 | .915 | .920 | **.925** | |
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| AffilGood-SPAN | .929 | .931 | **.938** | .927 | |
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### Citation |
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```bibtex |
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@inproceedings{duran-silva-etal-2024-affilgood, |
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title = "{A}ffil{G}ood: Building reliable institution name disambiguation tools to improve scientific literature analysis", |
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author = "Duran-Silva, Nicolau and |
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Accuosto, Pablo and |
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Przyby{\l}a, Piotr and |
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Saggion, Horacio", |
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editor = "Ghosal, Tirthankar and |
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Singh, Amanpreet and |
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Waard, Anita and |
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Mayr, Philipp and |
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Naik, Aakanksha and |
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Weller, Orion and |
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Lee, Yoonjoo and |
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Shen, Shannon and |
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Qin, Yanxia", |
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booktitle = "Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024)", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.sdp-1.13", |
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pages = "135--144", |
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} |
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``` |
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### Disclaimer |
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<details> |
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<summary>Click to expand</summary> |
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The model published in this repository is intended for a generalist purpose |
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and is made available to third parties under a Apache v2.0 License. |
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Please keep in mind that the model may have bias and/or any other undesirable distortions. |
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When third parties deploy or provide systems and/or services to other parties using this model |
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(or a system based on it) or become users of the model itself, they should note that it is under |
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their responsibility to mitigate the risks arising from its use and, in any event, to comply with |
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applicable regulations, including regulations regarding the use of Artificial Intelligence. |
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In no event shall the owners and creators of the model be liable for any results arising from the use made by third parties. |
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</details> |
