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A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays (AM_2022_coling2022)
We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components---premises, claims, and major claims---and the argumentative relations---premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation---in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.
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A Unified Representation and Deep Learning Architecture for Argumentation Mining of Students' Persuasive Essays (CMNA22)
We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components---premises, claims, and major claims---and the argumentative relations---premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation---in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. An augmentation of the corpus (Paragraph version) by including copies of major claims has further increased the performance.
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New augmented corpus are available in the datasets folder.
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The datasets (path: datasets/original) that we have used for detecting argument structures are taken from https://github.com/UKPLab/acl2017-neural_end2end_am/tree/master/data/conll
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Clone the repo:
git clone https://github.com/tawsifsazid/Unified-Representation-for-Argumentation-Mining.git
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Create an environment (Python 3.9.7) and run:
pip install -r requirements.txt
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Install PyTorch:
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
or from the official website -
The weight files can be downloaded from the links. Folder name 'weight' should contain the downloaded files before running the code.
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Run
python main.py
to reproduce the results -
Default config is set for original essay level corpus.
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@inproceedings{sazid-mercer-2022-unified, title = "A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students{'} Persuasive Essays", author = "Sazid, Muhammad Tawsif and Mercer, Robert E.", booktitle = "Proceedings of the 9th Workshop on Argument Mining", month = oct, year = "2022", address = "Online and in Gyeongju, Republic of Korea", publisher = "International Conference on Computational Linguistics", url = "https://aclanthology.org/2022.argmining-1.6", pages = "74--83", abstract = "We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components{---}premises, claims, and major claims{---}and the argumentative relations{---}premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation{---}in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that, a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.", }
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@inproceedings{sazid2022unified, title={A unified representation and deep learning architecture for argumentation mining of students’ persuasive essays}, author={Sazid, Muhammad Tawsif and Mercer, Robert E}, booktitle={to appear, CEUR Workshop Proceedings}, year={2022} }
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