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--- |
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license: cc-by-sa-4.0 |
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datasets: |
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- pie/sciarg |
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- DFKI-SLT/sciarg |
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language: |
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- en |
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--- |
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This is an argument structure prediction model for the scientific domain. It is a pointer network based on |
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[A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism |
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(Bao et al., EMNLP 2022)](https://aclanthology.org/2022.emnlp-main.713/). Given a plain input text, the model generates |
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in one go tuples that represent argumentative relations, e.g. of type `supports` or `attacks`, between a pair of |
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Argumentative Discourse Units (ADUs). Each ADU is defined by start- and end-offsets and a is also typed (`background_claim`, |
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`own_claim`, or `data`). |
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However, this is a full reimplementation of the model |
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within the [PyTorch-IE](https://github.com/ArneBinder/pytorch-ie) framework. The model source code can be |
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found in the [pie-modules](https://github.com/ArneBinder/pie-modules) repository. The model was trained with the |
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[PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) on the |
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[SciArg dataset](https://aclanthology.org/W18-5206/), see [here](https://huggingface.co/datasets/pie/sciarg) for |
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further information and an integration into [pie-datasets](https://github.com/ArneBinder/pie-datasets). Further |
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information regarding the training setup and model performance can be found in the [config.yaml](config.yaml), |
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in the [wandb-metadata.json](wandb-metadata.json), and in [wandb-summary.json](wandb-summary.json). |
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([link to private W&B run](https://wandb.ai/arne/dataset-sciarg-task-ner_re-v0.3-training/runs/wr3bg4la)) |
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You can try out the model in [this HF space](https://huggingface.co/spaces/ArneBinder/sam-pointer-bart-base-v0.3). |
