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<header>
<h1><a href="https://imagearg.github.io/">ImageArg-Shared-Task-2023</a></h1>
<p>Co-located with <a href="https://argmining-org.github.io/2023/">The 10th ArgMining Workshop</a> at <a
href="https://2023.emnlp.org/">EMNLP 2023</a> in Singapore. </p>
<p class="view"><a href="https://github.com/ImageArg/ImageArg-Shared-Task">View Project Code on GitHub
<small>ImageArg/ImageArg-Shared-Task</small></a></p>
<p class="view"><a style="color: red">Important Announcement:</a><br>
<b>[10/01]</b> Full corpus is now available <a href="https://github.com/ImageArg/ImageArg-Shared-Task">here</a>. <br>
<b>[09/01]</b> Paper submission due date is extended to 9/2 PM (AoE). <br>
<!-- <b>[08/28]</b> <s>No deadline extension - paper submission due date is still 9/1 (AoE).</s> <br>-->
<!-- <b>[08/19]</b> Paper submission portal is open! See "Call for Papers" section. <br>-->
<!-- <b>[08/02]</b> Call for system description papers! See "Call for Papers" section. <br>-->
<b>[08/01]</b> Shared task leaderboard is out! <br>
<b>[07/19]</b> Submission (predicted results) deadline is extended to 07/26 (AoE). <br>
<b>[07/17]</b> Task submission (predicted results) portal is open <a href="https://docs.google.com/forms/d/e/1FAIpQLScT-JU8FHu1Eoa1Eq1XzCfg9HBvFdoutxXL5lMTI6NnKTt6yg/viewform">here</a>.<br>
<!-- <b>[07/12]</b> The submission portal and evaluation script will be out by 7/16 as we received a large number of registrations that need us to refine the submission procedure. Thanks for your patience! <br>-->
<b>[07/08]</b> Test data is out <a href="https://github.com/ImageArg/ImageArg-Shared-Task"> here</a>.<br>
<!-- Evaluation script will be out soon.<br>-->
<!-- <b>[07/05]</b> Registration will be closed on 07/07 (AoE).<br>-->
<b>[07/03]</b> Update data-downloading scripts. Please pull the
<a href="https://github.com/ImageArg/ImageArg-Shared-Task"> new code</a>.<br>
<!-- <b>[05/20]</b> To participate, need to fill in this <a-->
<!-- href="https://docs.google.com/forms/d/e/1FAIpQLSci3TSw6ylcWnjXQsoUjh3buAQx7IdgiJwrJDR2pDHMm8DIpQ/viewform?usp=pp_url">registration-->
<!-- form</a>.-->
</p>
</header>
<section>
<h1 id="shared-task-on-predicting-validity-and-novelty-of-arguments">The First Shared Task in Multimodal
Argument Mining</h1>
<p>There has been a recent surge of interest in developing methods and corpora to improve and evaluate
persuasiveness in natural language applications. However, these efforts have mainly focused on the textual
modality, neglecting the influence of other modalities. <a
href="https://aclanthology.org/2022.argmining-1.1/">Liu et al.</a> introduced a new multimodal
dataset called <strong>ImageArg</strong>. This dataset includes persuasive tweets along with associated
images, aiming to identify the image's stance towards the tweet and determine its persuasiveness score
concerning a specific topic. The ImageArg dataset is a significant step towards advancing multimodal
persuasive text analysis and opens up avenues for exploring the persuasive impact of images in social media.
To further this goal, we designed this shared task, which utilizes the ImageArg dataset to advance
multimodal persuasiveness techniques.</p>
<p>Participants are welcome to submit system description papers for the shared task. Accepted papers will be
published in the proceedings of <a
href="https://argmining-org.github.io/2023/">The 10th ArgMining Workshop</a>.
To participate, please fill in this <a
href="Invalid">registration
form (Closed)</a> and feel free to join <a
href="https://join.slack.com/t/imagearg/shared_invite/zt-1ss5hdb6d-eNCaWOAEe4O_8UE1gQxIxA">ImageArg
Slack</a> for conversations.</p>
<hr>
<h2 id="dataset"> 1. The ImageArg Shared Task </h2>
The ImageArg dataset is composed of tweets (images and text) from controversial topics,
namely <strong> gun control </strong> and <strong> abortion</strong>. The dataset
contains 2-dimensions of annotations: Argumentative Stance (AS), Image Persuasiveness (IP), of which
each dimension addresses a unique research question: 1) AS: does the tweet have an
argumentative
stance? 2) IP: does the tweet image make the tweet more persuasive?<p></p>
<p>ImageArg Shared Task is divided into two subtasks. Participants can choose Task A or Task B, or both. Please
be aware that some tweet content may be upsetting or triggering. Please read details about AS and IP in the <a
href="https://aclanthology.org/2022.argmining-1.1">paper</a> if you
are interested.</p>
<h3 id="subtask-a-binary-novelty-validity-classification">Subtask A: Argumentative Stance (AS)
Classification</h3>
<p>Given a tweet composed of a text and image, predict whether the given tweet <strong> Supports </strong> or
<strong> Opposes </strong> the
given topic, which is a binary classification. Two examples are shown below. </p>
<img src="example_stance.png" alt="Example of the <strong> AS </strong> classification">
<p>The left tweets express strong stance towards support gun control by indicating a house bill about the
requirement of background check of all gun sales. The right tweet opposes gun control because it is inclined
to self-defense.</p>
<!-- <p>Please read the <a href="https://github.com/MeiqiGuo/ArgMining2022-ImageArg/">Data Description</a>-->
<!-- beforehand.</p>-->
<!-- <blockquote>
<p>If you use the data, please cite our <a href="https://aclanthology.org/">overview paper</a> and the <a href="https://aclanthology.org/">ImageArg paper</a> </p>
</blockquote> -->
<h4>Subtask A (AS Classification) Leaderboard</h4>
<table>
<thead>
<tr>
<th>Rank</th>
<th>Team Name</th>
<th>Attempt</th>
<th style="text-align: left;text-indent: 1em">Score</th>
</tr>
</thead>
<tbody>
<tr style="font-weight: normal">
<td>1</td>
<td>KnowComp</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em;">0.8647 ★</td>
</tr>
<tr>
<td>2</td>
<td>KnowComp</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.8571</td>
</tr>
<tr>
<td>3</td>
<td>KnowComp</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.8528</td>
</tr>
<tr style="font-weight: normal">
<td>4</td>
<td>Semantists</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.8506 ★</td>
</tr>
<tr>
<td>5</td>
<td>Semantists</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.8462</td>
</tr>
<tr>
<td>6</td>
<td>Semantists</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.8417</td>
</tr>
<tr>
<td>7</td>
<td>KnowComp</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.8365</td>
</tr>
<tr>
<td>8</td>
<td>Semantists</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.8365</td>
</tr>
<tr>
<td>9</td>
<td>Semantists</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.8365</td>
</tr>
<tr>
<td>10</td>
<td>KnowComp</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.8346</td>
</tr>
<tr style="font-weight: normal">
<td>11</td>
<td>Mohammad Soltani</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.8273 ★</td>
</tr>
<tr style="font-weight: normal">
<td>12</td>
<td>Pitt Pixels</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.8168 ★</td>
</tr>
<tr>
<td>13</td>
<td>Mohammad Soltani</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.8142</td>
</tr>
<tr>
<td>14</td>
<td>Mohammad Soltani</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.8093</td>
</tr>
<tr style="font-weight: normal">
<td>15</td>
<td>GC-HUNTER</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.8049 ★</td>
</tr>
<tr>
<td>16</td>
<td>Mohammad Soltani</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.8000</td>
</tr>
<tr>
<td>17</td>
<td>Pitt Pixels</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.7910</td>
</tr>
<tr>
<td>18</td>
<td>Mohammad Soltani</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.7782</td>
</tr>
<tr>
<td>19</td>
<td>GC-HUNTER</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.7766</td>
</tr>
<tr style="font-weight: normal">
<td>20</td>
<td>IUST</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.7754 ★</td>
</tr>
<tr>
<td>21</td>
<td>IUST</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.7752</td>
</tr>
<tr>
<td>22</td>
<td>Pitt Pixels</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.7710</td>
</tr>
<tr>
<td>23</td>
<td>Pitt Pixels</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.7415</td>
</tr>
<tr style="font-weight: normal">
<td>24</td>
<td>KPAS</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.7097 ★</td>
</tr>
<tr style="font-weight: normal">
<td>25</td>
<td>ACT-CS</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.6325 ★</td>
</tr>
<tr>
<td>26</td>
<td>ACT-CS</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.6178</td>
</tr>
<tr>
<td>27</td>
<td>ACT-CS</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.6116</td>
</tr>
<tr>
<td>28</td>
<td>ACT-CS</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.5863</td>
</tr>
<tr>
<td>29</td>
<td>IUST</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.5680</td>
</tr>
<tr>
<td>30</td>
<td>Pitt Pixels</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.5285</td>
</tr>
<tr style="font-weight: normal">
<td>31</td>
<td>feeds*</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.4418 ★</td>
</tr>
</table>
Note: Attempt denotes submission attempt number; Score denotes F1-score. The best submission attempt from each team is labeled with a star (★) and will be used for final ranking. *Team feeds submitted predictions for only one
topic by the submission deadline so only partial results are evaluated.
<p></p>
<h3 id="subtask-b-recognizing-relative-validity--novelty">Subtask B: Image Persuasiveness (IP)
Classification</h3>
<p>Given a tweet composed of text and image, predict whether the image makes the tweet text <strong>more
Persuasive</strong> or <strong>Not</strong>, which is a binary classification task. Two examples are shown
below.</p>
<img src="example_persuasiveness.png" alt="Example of the <strong> IP </strong> classification">
<p>The left tweet has an image not even relevant to gun control topic. It does not improve the
persuasiveness of the left tweet that argues to focus on mental health instead of gun restriction. The tweet
image
on the right makes the tweet text (and its stance) more persuasive because it provides strong evidence to
show the statistics of the murder
rate in major U.S. cities due to restrict gun control laws, so citizens cannot easily arm themselves.</p>
<h4>Subtask B (IP Classification) Leaderboard</h4>
<table>
<thead>
<tr>
<th>Rank</th>
<th>Team Name</th>
<th>Attempt</th>
<th style="text-align: left;text-indent: 1em">Score</th>
</tr>
</thead>
<tbody>
<tr style="font-weight: normal">
<td>1</td>
<td>feeds</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.5561 ★</td>
</tr>
<tr style="font-weight: normal">
<td>2</td>
<td>KPAS</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.5417 ★</td>
</tr>
<tr>
<td>3</td>
<td>feeds</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.5392</td>
</tr>
<tr style="font-weight: normal">
<td>4</td>
<td>Mohammad Soltani</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.5281 ★</td>
</tr>
<tr style="font-weight: normal">
<td>5</td>
<td>Semantists</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.5045 ★</td>
</tr>
<tr style="font-weight: normal">
<td>6</td>
<td>ACT-CS</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.5000 ★</td>
</tr>
<tr>
<td>7</td>
<td>Mohammad Soltani</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.4875</td>
</tr>
<tr>
<td>8</td>
<td>Mohammad Soltani</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.4778</td>
</tr>
<tr>
<td>9</td>
<td>Mohammad Soltani</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.4762</td>
</tr>
<tr>
<td>10</td>
<td>Semantists</td>
<td>5</td>
<td style="text-align: left;text-indent: 1em">0.4659</td>
</tr>
<tr style="font-weight: normal">
<td>11</td>
<td>IUST</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.4609 ★</td>
</tr>
<tr>
<td>12</td>
<td>Mohammad Soltani</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.4545</td>
</tr>
<tr>
<td>13</td>
<td>ACT-CS</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.4432</td>
</tr>
<tr>
<td>14</td>
<td>ACT-CS</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.4348</td>
</tr>
<tr>
<td>15</td>
<td>Semantists</td>
<td>4</td>
<td style="text-align: left;text-indent: 1em">0.4222</td>
</tr>
<tr>
<td>16</td>
<td>Semantists</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.4141</td>
</tr>
<tr style="font-weight: normal">
<td>17</td>
<td>KnowComp</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.3922 ★</td>
</tr>
<tr style="font-weight: normal">
<td>18</td>
<td>GC-HUNTER</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em">0.3832 ★</td>
</tr>
<tr>
<td>19</td>
<td>ACT-CS</td>
<td>2</td>
<td style="text-align: left;text-indent: 1em">0.3125</td>
</tr>
<tr>
<td>20</td>
<td>Semantists</td>
<td>3</td>
<td style="text-align: left;text-indent: 1em">0.2838</td>
</tr>
<tr style="font-weight: normal">
<td>21</td>
<td>Pitt Pixels</td>
<td>1</td>
<td style="text-align: left;text-indent: 1em"> 0.1217 ★</td>
</tr>
</table>
Note: Attempt denotes submission attempt number; Score denotes F1-score. The best submission attempt from each team is labeled with a star (★) and will be used for final ranking.
<br><br>
<hr>
<h2 id="paper accepted"> 2. Accepted Papers</h2>
The following system description papers submitted by the shared task participants have been peer-reviewed and accepted to the 10th ArgMining workshop, co-located at EMNLP 2023. The full proceedings are <a href="https://aclanthology.org/volumes/2023.argmining-1/" >here</a>.
</p>
<ul>
<li>A General Framework for Multimodal Argument Persuasiveness Classification of Tweets. Mohammad Soltani and Julia Romberg</li>
<li>IUST at ImageArg: The First Shared Task in Multimodal Argument Mining. Melika Nobakhtian, Ghazal Zamaninejad, Erfan Moosavi Monazzah and Sauleh Eetemadi</li>
<li>TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining. Qing Zong, Zhaowei Wang, Baixuan Xu, Tianshi Zheng, Haochen Shi, Weiqi Wang, Yangqiu Song, Ginny Wong and Simon See</li>
<li>Webis @ ImageArg 2023: Embedding-based Stance and Persuasiveness Classification. Islam Torky, Simon Ruth, Shashi Sharma, Mohamed Salama, Krishna Chaitanya, Tim Gollub, Johannes Kiesel and Benno Stein</li>
<li>GC-Hunter at ImageArg Shared Task: Multi-Modal Stance and Persuasiveness Learning. Mohammad Shokri and Sarah Ita Levitan</li>
<li>Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning. Arushi Sharma, Abhibha Gupta and Maneesh Bilalpur</li>
<li>SPLIT: Stance and Persuasion Prediction with Multi-modal on Image and Textual Information. Jing Zhang, Shaojun Yu, Xuan Li, Jia Geng, Zhiyuan Zheng and Joyce Ho</li>
<li>Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification. Kanagasabai Rajaraman, Hariram Veeramani, Saravanan Rajamanickam, Adam Maciej Westerski and Jung-Jae Kim</li>
</ul>
<hr>
<h2 id="scripts to download the data"> 3. Dataset and Shared Task Submission</h2>
<p>The dataset to download should only be used for scientific or research purposes. Any
other use is explicitly prohibited. The datasets must not be redistributed or shared in part or full with any third party per <a
href="https://developer.twitter.com/en/developer-terms/policy">Twitter
Developer Policy</a>. Redirect
interested parties to this website.
</p>
<p> All the tweets are instantly crawled from Twitter. Organizers are aware some tweets could not be available
when
participants start to download (e.g., a tweet could be deleted by its author). Organizers will regularly
monitor the
dataset to provide data patches that will replace invalid tweets with new annotated ones. Participants are
required to fill out the <a
href="https://forms.gle/ZJ2HFjSekXb8xkd87">Google
Form (Closed)</a> in order to receive data patches and the shared task updates.</p>
<p>Participants are allowed to extend only the training set with further (synthetic) samples. However, if do
that, participants have to describe and the algorithm which extends the training set in the system
description paper submission. This algorithm
must be automatically executable without any human interaction (hence, without further manual
annotation or manual user feedback).</p>
<p><strong>Shared Task Evaluation:</strong> <strong> F1-score </strong> of participating teams
will be used for ranking, but participants are free to include other metrics (e.g., AUC) in the system
description paper submissions. </p>
<p><strong>Shared Task Submission:</strong> There are up to 5 submissions from different approaches (systems)
allowed per team and per
subtask. Participants are allowed to withdraw your submission at anytime until the final
deadline by contacting the organizers. </p>
<ul>
<li>Training and dev data download: <a href="https://github.com/ImageArg/ImageArg-Shared-Task#Dataset">Here</a></li>
<li>Test data download: <a href="https://github.com/ImageArg/ImageArg-Shared-Task#Dataset">Here</a></li>
<li>Evaluation script (submission format validation): <a href="https://github.com/ImageArg/ImageArg-Shared-Task/blob/main/check_submission_format.py">Here</a></li>
<li>Shared Task Submission: <a href="https://docs.google.com/forms/d/e/1FAIpQLScT-JU8FHu1Eoa1Eq1XzCfg9HBvFdoutxXL5lMTI6NnKTt6yg/viewform">Here</a>.<br></li>
</ul>
<hr>
<h2 id="system-description-paper-submission">4. Call for Papers (System Description Papers)</h2>
<p>
The ImageArg Shared Task invites the submission of system description papers from all the teams
that have a successful submission to the leaderboard. Accepted papers will be published in the proceedings of <a
href="https://argmining-org.github.io/2023/">The 10th ArgMining Workshop</a>.
</p>
<!-- <h3 id="system-description-paper-submission-information">Paper Submission Information</h3>-->
<p>By default, we only accept short papers (at most 4 pages<s>, including references and optional appendix</s>).
References do not count the 4-page limit and Appendices have no page limit. Authors will have one
extra page to address reviewers' comments in their camera-ready versions. Please note that all papers will be
treated equally in the workshop proceedings. Authors are expected to adhere to the ethical code set out in the ACL
<a href="https://www.aclweb.org/portal/content/acl-code-ethics">Code of Ethics</a>. Submissions that
violate any of the policies will be rejected without review. </p>
<p><strong>At least one of the authors</strong> is required to be a reviewer and fill out this
<a href="https://forms.gle/2cWKEdUdb963oiAf7">Reviewer Form</a>. We will implement a double-blind review.</p>
<p>Structure of a system description paper could look as follows:</p>
<ul>
<li>Abstract</li>
<li>Introduction</li>
<li>Related work</li>
<li>Task/Data</li>
<li>Description of your approach</li>
<li>Experiments & Results
<ul style="margin: 0px">
<li>E.g., analyse your results and/or do an error analysis.</li>
<li>E.g., analyze the result across topics or within each topic.</li>
<li>E.g., add more experiments (if needed) based on your submission.</li>
</ul>
</li>
<li>Conclusion</li>
<li>References</li>
<li>Appendices (Optional)</li>
</ul>
<p><strong>Please cite the following two papers:</strong></p>
<div class="citation-container">
<pre class="snippet-clipboard-content notranslate position-relative overflow-auto" id="citation1" style="border: 0; padding: 20px">
@inproceedings{liu-etal-2022-imagearg,
title = "{I}mage{A}rg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining",
author = "Liu, Zhexiong and Guo, Meiqi and Dai, Yue and Litman, Diane",
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-1.1",
pages = "1--18"
}</pre>
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<pre class="snippet-clipboard-content notranslate position-relative overflow-auto" id="citation2" style="border: 0; padding: 20px">
@inproceedings{liu-etal-2023-overview,
title = "Overview of {I}mage{A}rg-2023: The First Shared Task in Multimodal Argument Mining",
author = "Liu, Zhexiong and Elaraby, Mohamed and Zhong, Yang and Litman, Diane",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = Dec,
year = "2023",
address = "Online and in Singapore",
publisher = "Association for Computational Linguistics"
}</pre>
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<p><strong>Paper Format</strong>: <a href="https://2023.emnlp.org/calls/style-and-formatting/">
EMNLP 2023 style sheets</a>.</p>
<p><strong>Paper Submission:</strong> <span style="color: red">Please select ImageArg Shared Task from the Submission
Categories dropdown</span> located in the middle of the <a
href="https://softconf.com/emnlp2023/ArgMining2023/user/scmd.cgi?scmd=submitPaperCustom&pageid=0">
Submission Form</a>; otherwise,
the organizers may not be able to receive your paper.
<!--Please use the CAMERA-READY, non-anonymized option.-->
<!-- <h3 id="paper-guidance">Suggested Paper Structure</h3>-->
<hr>
<h2 id="timeline">5. Timeline</h2>
<!-- The timeline for shared task and system description paper submissions.-->
<ul>
<li>05/15/23: Training and Dev scripts (data) released for both subtasks</li>
<li>07/07/23: Registration closed and Test scripts released for both subtasks</li>
<li><s>07/21/23</s>: 07/26/2023: Shared task submission due.</li>
<li>08/01/23: Leaderboard announcement for both tasks</li>
<li><s>09/01/23</s>: 09/02/23: System description paper due <span style="color: red"><s>(No Deadline Extension)</s></span></li>
<li>10/02/23: Notification of paper acceptance</li>
<li>10/09/23: Camera-ready paper due</li>
<li>12/10/23: Workshop dates</li>
</ul>
<hr>
<h2 id="terms-and-conditions">6. Terms and Conditions</h2>
<p>By participating in this task you agree to these terms and conditions. If, however, one or more of these
conditions is a concern for you, email us, and we will consider if an exception can be made.</p>
<ul>
<li>By submitting results to this competition, you consent to the public release of your scores at this
website and at ArgMining-2023 workshop and in the associated proceedings, at the task organizers’
discretion. Scores may include, but are not limited to, automatic and manual quantitative judgements,
qualitative judgements, and such other metrics as the task organizers see fit. You accept that the
ultimate decision of metric choice and score value is that of the task organizers.
</li>
<li>You further agree that the task organizers are under no obligation to release scores and that scores may
be withheld if it is the task organizers’ judgement that the submission was incomplete, erroneous,
deceptive, or violated the letter or spirit of the competition’s rules. Inclusion of a submission’s
scores is not an endorsement of a team or individual’s submission, system, or science.
</li>
<li>A participant can be involved in one team. Participating in more than one team is not recommended, but
not forbidden (if the person does not apply the same approach in different teams)
<ul>
<li>You must not use any data from the development split as training instances. You must not use any
test instance in the training of the model (also not indirectly for model selection). Approaches
that violate this data separation are disqualified.
</li>
<li>The usage of Large Language Models LLMs are permitted as long as they are freely available (eg.
LLAMA). Paid APIs for LLMs for (eg. openAI API) will be scored and reported but will not be
considered for final ranking to ensure fairness between teams who might not have these APIs
available.
</li>
</ul>
</li>
<li>Once the competition is over, we will release the gold labels, and you will be able to determine results
on various system variants you may have developed. We encourage you to report results on all of your
systems (or system variants) in the system-description paper. However, we will ask you to clearly
indicate the result of your official submission.
<ul>
<li>We will make the final submissions of the teams public at some point after the evaluation
period.
</li>
<li>The organizers and their affiliated institutions makes no warranties regarding the datasets
provided, including but not limited to being correct or complete. They cannot be held liable for
providing access to the datasets or the usage of the datasets.
</li>
<li>The dataset should only be used for scientific or research purposes. Any other use is explicitly
prohibited.
</li>
<li>The datasets must not be redistributed or shared in part or full with any third party. Redirect
interested parties to this website.
</li>
</ul>
</li>
</ul>
<hr>
<h2 id="task-organizers">7. Organizing Committee</h2>
<ul>
<!-- <ul>-->
<li><a href="https://people.cs.pitt.edu/~zhexiong/">Zhexiong Liu</a>, Ph.D. Student of Computer
Science
</li>
<li><a href="https://engsalem.github.io/">Mohamed Elaraby</a>, Ph.D. Student of Computer Science
</li>
<li><a href="http://yangzhongcs.com/">Yang Zhong</a>, Ph.D. Student of Computer Science</li>
<li><a href="https://people.cs.pitt.edu/~litman/">Diane Litman</a>, Professor of Computer Science
</li>
<li><strong>Contact</strong>: <span style="color: red">imagearg [at] gmail.com</span></li>
<!-- </ul>-->
<li><strong>Affiliation</strong>: University of Pittsburgh</li>
</ul>
<hr>
<h2 id="program-committee">8. Program Committee</h2>
<ul>
<li>Johannes Kiesel, Bauhaus-Universität Weimar</li>
<li>Arun Balajiee Lekshmi Narayanan, University of Pittsburgh </li>
<li>Melika Nobakhtian, Iran University of Science and Technology </li>
<li>Rajaraman Kanagasabai, Institute for Infocomm Research, A*STAR</li>
<li>Mohammad Shokri, Hunter College, CUNY</li>
<li>Mohammad Soltani, Heinrich Heine University</li>
<li>Ghazal Zamaninejad, Iran University of Science and Technology</li>
<li>Jing Zhang, Emory University</li>
<li>Qing Zong, Harbin Institute of Technology, Shenzhen</li>
</ul>
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