@inproceedings{torky-etal-2023-webis,
title = "{W}ebis @ {I}mage{A}rg 2023: Embedding-based Stance and Persuasiveness Classification",
author = "Torky, Islam and
Ruth, Simon and
Sharma, Shashi and
Salama, Mohamed and
Chaitanya, Krishna and
Gollub, Tim and
Kiesel, Johannes and
Stein, Benno",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.16",
doi = "10.18653/v1/2023.argmining-1.16",
pages = "157--161",
abstract = "This paper reports on the submissions of Webis to the two subtasks of ImageArg 2023. For the subtask of argumentative stance classification, we reached an F1 score of 0.84 using a BERT model for sequence classification. For the subtask of image persuasiveness classification, we reached an F1 score of 0.56 using CLIP embeddings and a neural network model, achieving the best performance for this subtask in the competition. Our analysis reveals that seemingly clear sentences (e.g., {``}I support gun control{''}) are still problematic for our otherwise competitive stance classifier and that ignoring the tweet text for image persuasiveness prediction leads to a model that is similarly effective to our top-performing model.",
}
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<abstract>This paper reports on the submissions of Webis to the two subtasks of ImageArg 2023. For the subtask of argumentative stance classification, we reached an F1 score of 0.84 using a BERT model for sequence classification. For the subtask of image persuasiveness classification, we reached an F1 score of 0.56 using CLIP embeddings and a neural network model, achieving the best performance for this subtask in the competition. Our analysis reveals that seemingly clear sentences (e.g., “I support gun control”) are still problematic for our otherwise competitive stance classifier and that ignoring the tweet text for image persuasiveness prediction leads to a model that is similarly effective to our top-performing model.</abstract>
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%0 Conference Proceedings
%T Webis @ ImageArg 2023: Embedding-based Stance and Persuasiveness Classification
%A Torky, Islam
%A Ruth, Simon
%A Sharma, Shashi
%A Salama, Mohamed
%A Chaitanya, Krishna
%A Gollub, Tim
%A Kiesel, Johannes
%A Stein, Benno
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F torky-etal-2023-webis
%X This paper reports on the submissions of Webis to the two subtasks of ImageArg 2023. For the subtask of argumentative stance classification, we reached an F1 score of 0.84 using a BERT model for sequence classification. For the subtask of image persuasiveness classification, we reached an F1 score of 0.56 using CLIP embeddings and a neural network model, achieving the best performance for this subtask in the competition. Our analysis reveals that seemingly clear sentences (e.g., “I support gun control”) are still problematic for our otherwise competitive stance classifier and that ignoring the tweet text for image persuasiveness prediction leads to a model that is similarly effective to our top-performing model.
%R 10.18653/v1/2023.argmining-1.16
%U https://aclanthology.org/2023.argmining-1.16
%U https://doi.org/10.18653/v1/2023.argmining-1.16
%P 157-161
Markdown (Informal)
[Webis @ ImageArg 2023: Embedding-based Stance and Persuasiveness Classification](https://aclanthology.org/2023.argmining-1.16) (Torky et al., ArgMining-WS 2023)
ACL