# Argument Relation Mining

Argument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 and the QT30 corpora. 

Extending the best performing RoBERTa-large model trained in the "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evaluation" paper.

macro-F1 (4-class classification): **0.70**


Conf. Matrix on test:

| Class         | **None** | **Inference** | **Conflict** | **Rephrase** |
|---------------|----------|---------------|--------------|--------------|
| **None**      | 2991     | 133           | 13           | 24           |
| **Inference** | 139      | 547           | 51           | 103          |
| **Conflict**  | 38       | 54            | 98           | 21           |
| **Rephrase**  | 55       | 128           | 25           | 443          |    


Cite:

```
@article{ruiz2021transformer,
author = {R. Ruiz-Dolz and J. Alemany and S. Barbera and A. Garcia-Fornes},
journal = {IEEE Intelligent Systems},
title = {Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation},
year = {2021},
volume = {36},
number = {06},
issn = {1941-1294},
pages = {62-70},
doi = {10.1109/MIS.2021.3073993},
publisher = {IEEE Computer Society}
}

```
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["argument mining"], "datasets": ["US2016", "QT30"], "metrics": ["macro-f1"]}
transformers pytorch roberta text-classification argument mining en dataset:US2016 dataset:QT30 license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #argument mining #en #dataset-US2016 #dataset-QT30 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

Argument Relation Mining
========================


Argument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 and the QT30 corpora.


Extending the best performing RoBERTa-large model trained in the "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evaluation" paper.


macro-F1 (4-class classification): 0.70


Conf. Matrix on test:



Cite:



TAGS
#transformers #pytorch #roberta #text-classification #argument mining #en #dataset-US2016 #dataset-QT30 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #text-classification #argument mining #en #dataset-US2016 #dataset-QT30 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
