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\title{NLPNCHU at SemEval-2024 Task 4: 
A Comparison of MDHC Strategy and In-domain Pre-training for Multilingual Detection of Persuasion Techniques in Memes}
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    \author{\textbf{Shih-Wei Guo$^1$}, \textbf{Yu-Ting Lin$^2$}, \textbf{Yu-An Lu$^3$},\textbf{Yao-Chung Fan$^1$$^*$}\\
  $^1$Department of Computer Science and Engineering, \\ National Chung Hsing University, Taiwan\\
  $^2$Taipei Municipal Chenggong High School, Taiwan\\
  $^3$National Chupei Senior High School, Taiwan\\
  \texttt{\{cometlcc,dong1214.mailbox,luyuam0\}@gmail.com, yfan@nchu.edu.tw}
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\begin{abstract}
% 本研究提出了一種系統描述，用於識別22種在多語言Meme中使用的說服性技巧。我們將官方提供的Meme說服性技術HDAG圖轉換成HMC(Multi-label Classification)任務，並嘗試了不同的微調技巧和分類策略，包括資料增強、問題變換和階層式多標籤分類策略等。識別Meme的說服性技巧是一個多模態任務。我們對特定領域的語言模型 XLM-RoBERTA-large-twitter\footnote{https://huggingface.co/sdadas/xlm-roberta-large-twitter\label{xlm-roberta-large-twitter}} 進行了微調，並結合了 CLIP 視覺模型的 embedding，同時關注圖像和文字特徵，成功識別Meme中的22種說服技巧。透過實驗使用官方的測試資料，包含了4種語言（英語、保加利亞語、北馬其頓語、阿拉伯語），我們的系統在大多數語言中總體排名名列前茅，在subtask1中阿拉伯語的測試數據集上排名第一。
This study presents a systematic method for identifying 22 persuasive techniques used in multilingual memes. We explored various fine-tuning techniques and classification strategies, such as data augmentation, problem transformation, and hierarchical multi-label classification strategies. Identifying persuasive techniques in memes involves a multimodal task. We fine-tuned the XLM-RoBERTA-large-twitter language model\footnote{https://huggingface.co/sdadas/xlm-roberta-large-twitter\label{xlm-roberta-large-twitter}}, focusing on domain-specific language modeling, and integrated it with the CLIP visual model's embedding to consider image and text features simultaneously. In our experiments, we evaluated the effectiveness of our approach by using official validation data in English. Our system in the competition, achieving competitive rankings in Subtask1 and Subtask2b across four languages: English, Bulgarian, North Macedonian, and Arabic. Significantly, we achieved 2nd place ranking for Arabic language in Subtask 1.
\end{abstract}

\section{Introduction}

%網絡的普及使得任何人都有可能成為新聞媒體。這一發展極其積極，因為它將言論自由提升到了全新的水平，讓每個人都有機會讓自己的聲音被聽到。隨著社交媒體的興起，每個人都可以接觸到非常廣泛的觀眾，這是以前只有主要新聞媒體才能做到的。然而，這也帶來了一些問題，例如宣傳或廣告中出現的許多虛假訊息，這可能影響公眾對事件和議題的理解。
% The widespread accessibility of the internet has democratized the field of news media, allowing anyone the potential to become a broadcaster of news. This shift is significantly positive, elevating the level of free speech to unprecedented heights and providing individuals the opportunity to have their voices heard. With the rise of social media, reaching a broad audience, once exclusive to mainstream media, is now possible for everyone. However, this evolution brings challenges, such as the spread of misinformation within propaganda and advertising, which can distort public understanding of events and issues.


% 宣傳和廣告都是說服性話語的例子。根據\cite{lakoff1982persuasive}的定義，說服性話語是指一方試圖或有意圖通過溝通手段改變另一方的行為、感覺、意圖或觀點，這是非互惠的。因此，除了溝通的純內容相關特徵外，話語背景在話語中也起著核心作用。
Propaganda and advertising serve as examples of persuasive discourse, which aims to change another's behavior, feelings, intentions, or views through communication, often in a one-sided manner \cite{lakoff1982persuasive}. Hence, the context in which the communication occurs is crucial alongside the actual content being conveyed.

% 宣傳活動旨在通過影響人們的思維方式來推進特定議程。它們利用網際網路的匿名性、社交網絡的微觀定位能力，以及自動創建和管理協調網絡帳戶的便利性，向數百萬社交網絡用戶發送具有說服力的消息，針對每個個體用戶敏感的話題，最終影響針對性問題的結果。
% Campaigns leverage the internet's anonymity, social networks' targeting capabilities, and the ease of managing coordinated network accounts to send persuasive messages to millions, affecting outcomes on sensitive topics.

% 在社交媒體平台上，迷因(Meme)搭配說服性話語在尤其有效。Meme是一種透過網路傳播想法或情感的媒體形式，是線上虛假資訊活動中最受歡迎的內容類型之一。因為在那裡它們可以輕鬆接觸到大量用戶。假訊息活動中的Meme透過多種修辭和心理技術（例如因果過度簡化、辱罵、誹謗）來實現影響使用者的目標。Meme由與文字疊加的圖像組成。圖像在欺騙性Meme中的作用要麼是強化/補充文本中的技巧，要麼是傳達一種或多種說服技巧。因此，Meme在塑造公眾觀點和態度方面具有重要作用。

Memes, combining persuasive discourse on social media platforms, prove particularly effective. They spread ideas or emotions online and are a popular tool in misinformation campaigns, using various rhetorical and psychological techniques to influence users. Memes' visual components either reinforce or convey persuasive tactics, thus playing a significant role in shaping public opinion and attitudes. To address these challenges, SemEval-2024 introduced a shared task focusing on detecting persuasion techniques from multilingual
memes \cite{semeval2024}. This task defines a hierarchy directed acyclic graph (HDAG) to represent a meme's persuasive techniques and highlights the challenges and importance of understanding the nuances of digital persuasion.

% \citetemp{(SemEval 2024 Task4, 2024)} 將 Meme 說服性技巧定義為一個 階層有向無循環圖 (Hierarchy Directed Acyclic graph, HDAG)，並且義了3個子任務：子任務 1: 僅根據 Meme 的文本內容，確定其使用了哪一種說服技巧，包含各層次的節點技巧。子任務 2a 和 2b: 輸入是一個包含文字和視覺內容的 Meme，屬於多模態任務。子任務2a\textcolor{red}{識別}\textcolor{green}{Meme}按照層次結構組織的22種說服技巧，而子任務2b則\textcolor{red}{識別} Meme 是否包含說服技巧，或者不包含任何技巧。

This study proposes exploring the effectiveness of multi-dimensional hierarchical classification (MDHC) strategies in identifying persuasive techniques in memes, based on previous research and the successful application of MDHC strategies in real-world HDAGs. The results from a competition show our approach's effectiveness, ranking first in a specific subtask and competitively across others, demonstrating the potential of MDHC strategies in analyzing persuasive discourse.

%我們基於先前研究提出將HDAG轉換成分層多標籤分(HMC)任務\cite{bi2011multi,ramirez2014multi,zhang2017hierarchical}，Hierarchical Multi-label Classification (HMC) is the task of assigning a set of classes to a single instance with the peculiarity that the classes are ordered in a predefined structure. \cite{montenegro2023introducing} 提出MDHC策略成功在現實HDAG獲得最佳分數，因此我們基於採用兩種多維階層分類問題(MDHC)策略，目的是探討MDHC的分類策略對說服力技巧的有效性。

%競賽的結果顯示，我們在subtask 1阿拉語測試集上的官方指標Hierarchy$_{F_1}$分數排名第一，在其他任務及語言上具有競爭力表現，平均排名第四。


\begin{figure*}[htb]
     \centering
     \includegraphics[width=1.6\columnwidth]{latex/images/The Tree of Persuasion Techniques-en.png}
     \caption{Hierarchy Multi-label Classification(HMC) with Persuasive Techniques}
     \label{fig:The_Tree_of_Persuasion_Techniques}
\end{figure*}
\section{Background}
In this study, we explore the application of Hierarchical Multi-label Classification (HMC) in the context of persuasive techniques, by structuring them within a hierarchical multi-label framework. This approach allows for the simultaneous handling of both textual and visual data through multimodal modeling. 

% 最近的研究 \cite{montenegro2023introducing} 已證明了多維分層分類（MDHC）方法在HMC方面的有效性，指出其簡單性和易於實施。 HMC是對多標籤分類（MC）的進步，旨在預測從一般到特定類別按層次組織的多個標籤。 層次知識的融入被發現可以顯著提高分類器的性能。 MC適用於各種領域，涉及預測多個相關聯的類別變量帶來挑戰。 正如Alfaro等人 \cite{alfaro2023multi} 和Bielza和Larrañaga所強調 \cite{bielza2011multi} ，與單一維度問題相比，MC的複雜性主要源自大量不同組合的類別標籤以及相關數據稀缺。
Recent research \cite{montenegro2023introducing} has demonstrated the efficacy of the MDHC approach for HMC, noting its simplicity and ease of implementation. HMC, an advancement of Multi-label Classification (MC), is designed to predict multiple labels that are organized hierarchically from general to specific categories. The incorporation of hierarchical knowledge is found to significantly improve the performance of classifiers. 

The MC, which is applicable in a wide range of areas, involves the challenge of predicting multiple interrelated category variables. As highlighted by Alfaro et al. \cite{alfaro2023multi} and Bielza and Larrañaga \cite{bielza2011multi}, the complexity of MC compared to single-dimensional problems is primarily due to the vast combinations of class labels and the scarcity of relevant data.


We transform persuasive techniques into the HMC framework, this approach transforms persuasive technique graphs for application in specific subtasks. Given the necessity to analyze both textual and visual data for accurately identifying persuasive techniques, multimodal models become essential. 

CLIP (Contrastive Language-Image Pre-Training)  proposed by OpenAI \cite{radford2021learning}, stands out for its independent text and image encoding capabilities, offering flexibility for various subtask types. In a study, \cite{kumar-nandakumar-2022-hate} have suggested a range of techniques that combine textual and visual embedding vectors, leading to the effective detection of hateful memes. They also conducted various fusion experiments by switching different text encoders. Therefore, we refer to the authors' approach to combine the embedding vectors of the CLIP and the multilingual model with the aim of better adapting to Subtask 2ab, which involves tasks belonging to the multilingual domain and including datasets in three non-English languages (Bulgarian, North Macedonian, Arabic) in the test set. 

\section{Exploratory Data Analysis for Datasets}

The dataset used in this study contains about 15,000 memes in English and other languages.

We have examined the label distribution for each task, and it is apparent that the data sets for subtask 1 and 2a are imbalanced, which differing numbers of Memes's persuasive techniques available for positive and negative are shown in Figure \ref{fig:Data_distribution_of_P.T._in_subtask-1_train_set} and Figure \ref{fig:Data_distribution_of_P.T._on_subtask_2a_train set}. Further scrutiny, as delineated in Attachment Figure \ref{fig:Ratio_of_the_Persuasion_Techniques_for_all_subtask},  it's evident that the datasets for Subtask 1 and 2a have a highly imbalanced distribution of data across 22 persuasion techniques, with Subtask 2a, in particular, showing a significant imbalance between positive and negative samples.
We will describe how to address these imbalances in subsequent sections.
%從附件中Figure \ref{fig:Rate_of_the_Persuasion_Techniques_for_all_subtask} 可得知 subtask 1和2a 的 datset 在22種說技巧的資料分佈非常不平衡，尤其在 subtask 2b 呈現正樣本和負樣本出現嚴重失衡的分佈，我們將於後續章節描述如何處理這些失衡的問題。


\begin{figure}[tb]
     \centering
     \includegraphics[width=0.9\columnwidth]{latex/images/Data distribution of P.T. in subtask 1 train set.png}
     \caption{Data distribution of Persuasive Techniques on Subtask 1 Train Set}
     \label{fig:Data_distribution_of_P.T._in_subtask-1_train_set}
\end{figure}


\subsection{Transform the Structure of the Persuasive Techniques}
% 官方提供一張包含22種說服性的HDAG，我們將階層圖重構為一個層級多標籤分類(HMC)。如Figure \ref{fig:The_Tree_of_Persuasion_Techniques}顯示，我們重構的層級多標籤分類(HMC)共有三個階層，其中有1個根節點，第一層有3個子節點，第二層有5個子節點，底部有22個葉節點。

The official release includes HDAG comprising 22 types of persuasive techniques. We have transformed this hierarchy into HMC. As shown in Figure \ref{fig:The_Tree_of_Persuasion_Techniques}, our reconstructed HMC has three levels: it includes 1 root node, the first layer has 3 child nodes, the second layer has 5 child nodes, and the bottom layer consists of 22 leaf nodes : 

% \paragraph{根節點:} 描述一張 Meme 影像是否擁有說服技巧。\paragraph{第1層節點:}這裡共有3個子節點，分別為 Ethos、Logos、Pathos，這三個節點將22種說服技巧分成3種說服技巧類別。
% \paragraph{第2層節點:}這裡共有5個子節點，分別為 Ad Hominem、Justification、Distraction、Simplification 和 Other，我們使用 Other 節點名稱簡化官方的說服技巧階層圖中 Distraction和Simplification節點，這是因為在MDHC策略上它們是多餘的節點。\paragraph{葉節點:}這裡共有22個節點，對應到的是本次任務最關注的22種說服技巧。其中，一個說服性技巧在擁有多個第一層類別時採用相同顏色呈現。
\begin{itemize}
    \item \textbf{Root}: This describes whether a Meme image possesses any persuasive techniques.
    \item \textbf{First Layer Nodes}: There are 3 child nodes at this layer: Ethos, Logos, and Pathos. These nodes categorize the 22 types of persuasive techniques into 3 distinct classes of persuasive strategies. 
    \item \textbf{Second Layer Nodes}: This layer includes 5 child nodes: Ad Hominem, Justification, Distraction, Simplification, and Other. We simplify the official hierarchy of persuasive techniques by using the "Other" node to encompass the Distraction and Simplification nodes, as they are redundant in the MDHC strategy.
    \item   \textbf{Leaf Nodes}: There are 22 nodes at this level, corresponding to the 22 types of persuasive techniques that are the focus of this task. When a persuasive technique belongs to multiple categories in the first layer, it is represented by the same color.
\end{itemize}



\begin{figure}[htb]
     \centering
     \includegraphics[width=0.9\columnwidth]{latex/images/MDC task in Multimodal workflow.png}
     \caption{The Workflow for Multiclass Classification Task on the Multimodal Model }
     \label{fig:MDC_task_in_Multimodal_workflow}
\end{figure}

\section{System Overview}
% 在這項研究中，我們評估並比較了兩種不同的MDHC方法，即為 Stacking+GC和Stacking+LCL，這個比較是在相同數據集上對訓練好的模型進行的。我們認為，兩種方法在識別每個層級關聯方面存在差異，這導致合併時的誤差產生差異。我們的結果表明，Stacking+GC優於Stacking+LCL。

In our research, we conducted an in-depth comparison of two MDHC strategies: Stacking+GC and Stacking+LCL, utilizing the same dataset for model training. The comparative analysis revealed that Stacking+GC demonstrated superior performance over Stacking+LCL. This superiority is attributed to its more effective handling of errors during the merging process of hierarchical levels, thereby enhancing the overall classification accuracy within the hierarchical structure of the data.

% \paragraph{XLM-RoBERTA-large-twitter\footref{xlm-roberta-large-twitter}} 本次系統任務是一種多語言任務，Meme是屬於社群媒體特定領域，因此我們對特定領域的語言模型 XLM-RoBERTA-large-twitter 進行了微調，該模型根據十種語言的超過 1.56 億條推文的語料庫進行了微調。

% \paragraph{CLIP} \cite{radford2021learning} 使用兩種獨立的架構作為編碼視覺和文字資料集的主幹：\textbf{image encoder}：代表負責編碼影像的神經網路架構（例如，ResNet 或 Vision Transformer）。 \textbf{text encoder}：代表負責編碼文字訊息的神經網路架構（例如，BERT 或 Text Transformer）。這個架構可以靈活的適應本次的子任務。

\paragraph{XLM-RoBERTA-large-twitter\footref{xlm-roberta-large-twitter}} For this system task, which is multilingual and specifically focused on the social media domain of Memes, we fine-tuned the domain-specific language model XLM-RoBERTA-large-twitter. This model was adjusted based on a corpus of over 156 million tweets in ten languages.

\paragraph{CLIP} uses two distinct architectures as the backbone for encoding visual and textual datasets: image encoder, which represents the neural network architecture responsible for encoding images (e.g., ResNet or Vision Transformer), and text encoder, which represents the neural network architecture responsible for encoding textual information (e.g., BERT or Text Transformer). This structure is flexibly adapted to the subtasks of this project.

% Multi-dimensional hierarchical classification is the Multi-dimensional classification(MDC) and Hierarchical classification(HC) 的一種變形任務。

% MDHC is a supervised learning paradigm in which multiple class variables (dimensions)
% \subsection{MDHC策略詳細描述}
\subsection{Detailed Description of the MDHC Strategy}
\paragraph{MDHC Strategies} 
% An algorithm designed to solve a HC problem can be described as 4 types:


% The MDC strategies have been chosen to represent the most frequently used types of classification strategies in the literature:

\begin{figure}[htb]
     \centering
     \includegraphics[width=1\columnwidth]{latex/images/MDHC Strategy.png}
     \caption{Illustration of the two DMC strategies. The feature vector is used on the $1^{st}$ phase as input, while the $2^{st}$ phase uses the outputs of the $1^{st}$ phase. it is to solve all the tasks associated with the internal nodes drawn inside the circle.}
     \label{fig:MDHC_Strategy}
\end{figure}

% In order to comparetion MDHC classification strategies, we combine a MDC strategies with two HC strategies to generate two different proposals. 

% 我們參考\cite{montenegro2023introducing}提出的MDHC paradigm中，選出適合本次任務的HMC分類策略，這個方法需要同時考續MC和HC的分類策略。挑選解決 MC 問題的算法有兩種類型： \textbf{1.LCL（Local Classifier per Level）}：為層次結構的每個級別創建一個模型。\textbf{2.GC（Global Classifier）}：一種學習整個類別層次結構並同時對所有節點進行預測的模型。
% 接著解決 HC 分類策略可以採用\cite{wolpert1992stacked}提出的堆疊（Stacking）：用其他標籤的預測資訊來修正透過單獨學習每個標籤所獲得的原始預測結果。在我們策略中修正方式採用模型預測的信心度取平均，大於0.5即判別存在特定說服技巧。
Incorporating the MDHC paradigm proposed by \cite{montenegro2023introducing}, our strategy selects an HMC classification approach suitable for this task, integrating both MC and HC strategies. 

In MC strategy, we explore two solution algorithms: \textbf{Local Classifier per Level (LCL)}, which creates a model for each level of the hierarchy, and \textbf{Global Classifier (GC)}, a model that learns and predicts across the entire class hierarchy.

For HC strategy, we adopt \textit{Stacking}, as introduced by \cite{wolpert1992stacked}, leveraging predictions from other labels to refine initial predictions, using average confidence scores to identify specific persuasive techniques with a threshold of 0.5 for determination. 

To compare MDHC classification strategies, we integrate two MC strategies with an HC strategy, resulting in two distinct approaches.

% Stacking: replace the original predictions, obtained by learning every label separately, by correcting them in light of information about the predictions of the other labels [44]. This transformation of the initial prediction should be interpreted as a regularization procedure: a bias is introduced, in an attempt to decrease the variance.

% Figure \ref{fig:MDHC_Strategy}顯示，為了比較 MDHC 分類策略，我們將兩種 MC 策略與一種 HC 策略結合，生成了兩個不同的策略。



% \paragraph{Stacking  + GC}: 在第一階段中，對每個維度分別應用GC策略，使用特徵向量作為輸入；在第二階段採用Stacking策略，以串聯第一階段分類器預測結果的概率向量作為結果輸出。這種MDHC策略如圖5所示，應用於圖3中所示的HMC問題，其中每個圓代表解決列出的父節點的分類問題的分類器。

\textbf{Stacking + GC} : this strategy applies the GC strategy to each dimension in the $1^{st}$ phase, utilizing feature vectors as inputs. The $2^{nd}$ phase employs the Stacking strategy, concatenating the probability vectors of the $1^{st}$ phase classifier's predictions for output. This strategy is shown in a Figure \ref{fig:MDHC_Strategy} (a)

% \paragraph{Stacking  + LCL}: 在第一階段中，對每個維度分別應用LCL策略，使用特徵向量作為輸入；在第二階段在第二階段採用Stacking策略，以串聯第一階段分類器預測結果的概率向量作為結果輸出。這種MDHC策略如圖5所示，應用於圖3中所示的MDHC問題，其中每個圓代表解決列出的父節點的分類問題的分類器。

\textbf{Stacking + LCL} : this strategy applies the LCL strategy to each dimension in the $1^{st}$ phase. In the $2^{nd}$ phase, it also adopts the Stacking strategy, using concatenated probability vectors from the first stage's predictions, with each circle representing a classifier addressing the classification problem of listed parent nodes. This strategy is shown in a Figure \ref{fig:MDHC_Strategy} (b) 

\subsection{Internal  Negative Data Augmentation} % 在一般的資料集中，通常會包含相似比例正負樣本，儘管它們可能不會呈現平衡分佈。然而，在subtask 2b的資料分佈中，僅包含2筆不包含說服力的樣本。一般來說，我們可以使用PLC\footnote{https://propaganda.math.unipd.it/ptc/}資料集來擴充，然而PTC資料集是包含18種說服技巧的新聞句子，然而，在階層多標籤分類中的多標籤存在複雜關聯，若僅擴充少數標籤樣本，可能導致其他樣本標籤嚴重失衡。

In typical datasets, there is usually a similar ratio of positive to negative samples, even though they may not be evenly distributed. However, in the data distribution for Subtask 2b, there are only 2 samples that do not contain persuasive elements. Generally, one could use the PTC\footnote{https://propaganda.math.unipd.it/ptc/} dataset to augment this. However, the PTC dataset consists of news sentences with 18 types of persuasive techniques, and in hierarchical multi-label classification, the multiple labels have complex relationships. 

When attempting to augment data by adding more examples for underrepresented labels, one must navigate the complex interplay between these labels carefully. Simply increasing the number of samples for a specific label can inadvertently exacerbate the imbalance for others. For example, if we augment the dataset with more instances of the 'Whataboutism' technique without considering its relationship with other techniques like 'Loaded Language' or 'Flag-waving,' we might skew the dataset further, making it even more challenging to train a balanced and accurate classifier.

% 基於上述問題，我們採用了兩個MDHC策略，將整個說服技巧階層基於第二層父節點（Ad Hominem、Justification、Distraction、Simplification和Other）拆分為六個MC任務。其中，Other可以拆分為兩個任務。每個任務獨立執行MC策略，將自己所屬的說服技巧視為正樣本，其餘視為負樣本，這個方法我們稱為 Internal  Negative Data Augmentation，這個方法不僅提供有效的負樣本，還可以避免不同數據集之間標記不一致問題。最後，這六個MC任務通過投票方式決定最上層父節點（有無說服技巧）的標籤。

To address this issue, we used two MDHC strategies. Firstly, we divided the entire hierarchy of persuasive techniques into six tasks based on the second-layer parent nodes: Ad Hominem, Justification, Distraction, Simplification, and Other (which was further split into two tasks). Each task independently applies the MC strategy, considering its specific persuasive techniques as positive examples and the others as negative examples, which approach we called \textbf{Internal Negative Data Augmentation(INDA)}, not only offers effective negative examples but also ensures consistency in labeling across various datasets. Ultimately, these six MC tasks determine the classification of the top-level parent node (indicating the presence of persuasive techniques) through a voting mechanism.

%然而，INDA方法雖然解決的在無說服服技巧中標籤分佈不平衡問題，卻帶來 Long Tail Distribution 問題。
%Long Tail Distribution 是一種機率分佈的模型，它的尾部的機率密度較小。在許多狀況中，通常右邊尾部的分布會比較受到重視，但左邊尾部機率密度較大。從附件中 Figure \ref{fig:Data_distribution_of_MC_for_P.T._of_Ethos_in_the_MDHC_strategy},  \ref{fig:Data_distribution_of_MC_for_P.T._of_Logos_in_the_MDHC_strategy} and  \ref{fig:Data_distribution_of_MC_for_P.T._of_Pathos_and_Logos_in_the_MDHC_strategy} 顯示，我們採用的MDHC分類策略使得一種說服技巧類型受關注正樣本分佈在尾部，形成一個偏左的長尾分佈。

However, the INDA, while addressing the issue of imbalanced label distribution among samples without persuasive techniques, introduces the Long Tail Distribution problem. Long Tail Distribution is a probability distribution model characterized by lower probability density in its tail. In many cases, the distribution's right tail is considered more significant, but the left tail has a higher probability density. As shown in Attachment Figures \ref{fig:Data_distribution_of_MC_for_P.T._of_Ethos_in_the_MDHC_strategy},  \ref{fig:Data_distribution_of_MC_for_P.T._of_Logos_in_the_MDHC_strategy} and  \ref{fig:Data_distribution_of_MC_for_P.T._of_Pathos_and_Logos_in_the_MDHC_strategy}, the MDHC classification strategy we employed results in the positive distribution of a particular persuasive technique type being concentrated in the tail, forming a left-skewed long tail distribution.

% 長尾分佈存在著兩個問題，Label co-occurrence 和 Dominance of negative labels:
% Label co-occurrence : 文本和多個說服技巧有關連，無法對某一個類別的分步進行精準的採樣處理。
% Dominance of negative labels : 一個文本只會和某一小部分說服技巧有關聯，因此絕大部分的標籤都是負關聯，然而BCE對於正負關聯類別採平均處理，這會產生負關聯的邊界偏移。

The long-tail distribution presents two main challenges: Label co-occurrence and Dominance of negative labels: 

\textbf{Label co-occurrence} : texts are often associated with multiple persuasive techniques simultaneously, making it difficult to accurately sample individual categories. 

\textbf{Dominance of negative labels} : a text may only be associated with a small subset of persuasive techniques, resulting in the majority of labels being negative. However, Binary Cross-Entropy (BCE) treats positive and negative associated categories equally, leading to a shift in the boundary of negative associations.


% 為了解決Label co-occurrence 和 Dominance of negative labels 兩個問題，我們引入\cite{wu2020distribution} 提出的 Distribution-Balanced Loss (DBL)，這個損失函數採用 re-balanced weighting 和 negative-tolerant regularization 解決Label co-occurrence和Dominance of negative labels 這兩個問題。

To address the issues of Label co-occurrence and Dominance of negative labels, we introduce the Distribution-Balanced Loss (DBL) proposed by \cite{wu2020distribution}, the loss function employs re-balanced weighting and negative-tolerant regularization to mitigate the challenges posed by Label co-occurrence and Dominance of negative labels.

% 因此，在不依賴外部數據增強的情況下，我們使用MDHC策略結合DBL損失函數，來解決subtask 1和2a的樣本不平衡問題。同時，我們也引入DBL損失函數來處理長尾分佈的問題。

Therefore, without relying on external data augmentation, we utilize the MDHC strategy combined with the DBL loss function to address the sample imbalance issues in Subtask 1 and 2a. Additionally, we also introduce the DBL loss function to tackle the long-tail distribution problem.

\subsection{Meme with Multimodal Learning} In Subtask 2a and 2b, we employ the CLIP multimodal model, which includes an image encoder and a text encoder. The image encoder is responsible for encoding images, while the text encoder handles encoding textual information. At this stage, we utilize the XLM-RoBERTA-twitter fine-tuned on Subtask 1 as the text encoder because this model already possesses a certain understanding of meme text. For the image encoder, we use the pre-trained CLIP image encoder provided by the official source. Through Feature-wise Linear Modulation (FIM), these two encoders encode to obtain a representation embedding vector containing both image and text, enabling the model to effectively comprehend memes reliant on the relationship between text and image, such as persuasive techniques like Transfer and Appeal to (strong) Emotions.

% 在Subtask 2a和2b中，我們採用了CLIP多模態模型，它包含image encoder和text encoder。image encoder負責編碼影像的神經網路架構，而text encoder負責編碼文字訊息的神經網路架構。在這個階段，我們使用在Subtask 1微調過的XLM-RoBERTA-twitter作為text encoder，因為這個模型已經對Meme文字有一定的理解能力。而image encoder則採用官方已經預訓練好的CLIP image encoder。透過FIM計算這兩個encoder編碼得到一個同時包含圖像和文字的表示嵌入向量，使得模型有效理解只有依賴圖文關係的Meme技術，例如Transfer和Appeal to (strong) Emotions說服性技術。

% 具體來說，如圖 \ref{fig:MDC_task_in_Multimodal_workflow}呈現，Subtask 2ab 採用 image encoder 針對Meme影像進行編碼 $I$，text encoder 針對Meme影像進行編碼 $T$，
% 因此可以得到影像編碼向量 $I_1...I_N$ 以及文字編碼向量 $T_1...T_N$。
% 我們將兩個向量 $I$和$T$進行FIM的計算，FIM採用兩個向量相乘計算法，得到一組新的特徵向量。接著我們在模型的後方接上一個用於分類的線性層，用來輸出正確的分類。具體來說，在Subtask 2a學習目標為多標籤(MC)的說服技巧，而Subtask 2b學習目標為二元分類，識別它是否包含說服技巧，還是不包含技巧。

Specifically, as depicted in Figure \ref{fig:MDC_task_in_Multimodal_workflow}, Subtask 2ab involves encoding Meme images using the image encoder ($I$) and Meme text using the text encoder ($T$). Thus, we obtain sets of image encoding vectors $I_1...I_N$ and text encoding vectors $T_1...T_N$. We compute the FIM by multiplying these two vectors to obtain a new set of feature vectors. Subsequently, we attach a linear layer for classification at the end of the model to output the correct classifications. Specifically, in Subtask 2a, the learning objective is multi-label (MC) persuasion techniques classification, while in Subtask 2b, the learning objective is binary classification to identify whether it contains persuasive techniques or not.

\section{Experiment and Evaluate}
% 我們的實驗中，採用MDHC分類策略有三種: 第一種策略:GC策略是將Figure \ref{fig:The_Tree_of_Persuasion_Techniques}中說服力階層樹採用MC任務進行訓練和預測，這是最直接的方式。
% 第二種策略 : Stacking + GC 策略是基於第二層中的三個子節點創建三個模型，第一階段時每一個模型皆採用 GC 策略預測，接著因為這三個階層樹之間存在重複地說服技巧，因此第二階段透過Stacking策略將其預測的資訊修正其他所有樹的預測結果。第三種策略:Stack + LCL策略是將每一層獨立創建一個模型，會創建三個模型，第一階段時每一個模型採用LCL策略預測，最後第二階段採用Stacking策略將其預測的資訊修正其他所有樹的預測結果。所有任務皆採用 Hierarchy_${F_1}$，這是官方提供的統一評估指標。

% In our experiments, we employed three MDHC classification strategies:\textbf{GC Strategy:} The persuasion hierarchy tree shown in Figure \ref{fig:The_Tree_of_Persuasion_Techniques} is trained and predicted using MC tasks. This is the most straightforward approach. \textbf{Stacking + GC Strategy:} In this strategy, three models are created based on the three child nodes in the second layer. In the first stage, each model makes predictions using the GC strategy. In the second stage, since there are overlapping persuasive techniques among these three hierarchy trees, the Stacking strategy is used to adjust the predictions of each model and rectify the predictions of all other trees. \textbf{Stacking + LCL Strategy:} Each layer is independently modeled, resulting in the creation of three models. In the first stage, each model makes predictions using the LCL strategy. Finally, in the second stage, the Stacking strategy is utilized to adjust the predictions of each model and refine the predictions of all other trees.
In our experiments, we employed three MDHC classification strategies on subtasks.
All tasks utilize the Hierarchy_${F_1}$ evaluation metric, which is the unified evaluation metric provided by the official source. In the experiment setting, refer to Appendix \ref{apix:implementation_details} for details of the relevant parameters.

In \textbf{Subtask 1}, we compared the performance of two language models, XLM-RoBERTA and XLM-RoBERTA-Twitter, across Subtask 1 and 2a, to assess the impact of domain-specific pre-training. We explored three classification strategies: GC, Stacking + GC, and Stacking + LCL, to identify the most effective approach for Subtask 1. In \textbf{Subtask 2a}, we evaluated two multimodal models by combining CLIP with XLM-RoBERTA-Twitter from Subtask 1. The goal was to improve the comprehension of Meme's persuasive techniques by utilizing a fine-tuned text encoder. Additionally, we employed GC, Stacking + GC, and Stacking + LCL strategies to determine which one is more effective for subtask 2a.  Our primary focus was on improving classification performance in a multimodal setting. In \textbf{Subtask 2b}, we explored multimodal model and classification strategy using the CLIP + XLM-RoBERTA-Twitter combination from subtask 1, applied to a binary classification framework. Utilizing a balanced dataset of hate Memes collected by Meta AI, as referenced in the Harmful Memes Dataset \cite{kiela2020hateful}, the goal was to train the model on balanced samples to prevent bias effectively.

% 在\textbf{subyask 2a} 實驗兩種多模態模型三種分類策略，模型採用CLIP + XLM-RoBERTA-twitter(subtask 1)，這樣的組合目的是期望經過subtask 1微調過的text encoder可以讓模型更好理解Meme說服技巧。此任務也實作 Stacking + GC 和 Stack + LCL ，目的是比較這兩個策略哪一個比較合 subtask 2a 。在\textbf{subtask 2b} 實驗一種多模態模型和一種分類策略，一種分類策略。此任務以簡單的方式來完成，模型採用CLIP + XLM-RoBERTA-twitter(subtask 1)實作在一個二元分類模型上，從\cite{kiela2020hateful} Meta AI 收集的仇恨Meme資料平衡正負樣本比例，目的是為了使用平衡的樣本來訓練模型，可有效的避免模型產生偏差。


% Please add the following required packages to your document preamble:
% \usepackage{multirow}
\begin{table}[htb]
\centering
\caption{Performance for Subtask 1 in Validation Set}
\label{tab:Performance for Subtask 1}
\resizebox{\columnwidth}{!}{%
    \begin{tabular}{cclll}
    \hline
    \multirow{2}{*}{\textbf{Models}}                                                              & \multirow{2}{*}{\textbf{Strategy}} & \multicolumn{3}{c}{\textbf{Metrics}}                                      \\ \cline{3-5}                                                                                          
                                                                                         &                           & \multicolumn{1}{l}{\textbf{H-F1}}  & \multicolumn{1}{l}{\textbf{H-Prec}} & \textbf{H-Rec} \\ \hline
    Baseline                                                                             & Official                  & \multicolumn{1}{l}{0.3651} & \multicolumn{1}{l}{0.4573}  & 0.3038 \\ \hline
    \multirow{3}{*}{\makecell[c]{XLM-R Large}}         & GC                        & \multicolumn{1}{l}{0.5594} & \multicolumn{1}{l}{0.4635}  & 0.6335 \\  
                                                                                         & Stacking   + LCL          & \multicolumn{1}{l}{0.5995} & \multicolumn{1}{l}{0.5244}  & 0.6909 \\  
                                                                                         & Stack   + GC              & \multicolumn{1}{l}{0.6262} & \multicolumn{1}{l}{0.5907}  & 0.6646 \\ \hline
    \multirow{3}{*}{\makecell[c]{XLM-R-Large Twitter}} & GC                        & \multicolumn{1}{l}{0.5580}  & \multicolumn{1}{l}{0.6367}  & 0.6503 \\  
                                                                                         & Stacking   + LCL          & \multicolumn{1}{l}{0.6310}  & \multicolumn{1}{l} {0.6062}  & 0.6764 \\  
                                                                                         & Stacking   + GC           & \multicolumn{1}{l}{\textbf{\underline{0.6689}}} & \multicolumn{1}{l}{\textbf{\underline{0.6843}}}  & \textbf{\underline{0.7451}} \\ \hline
    \end{tabular} %
}
\end{table}

% Please add the following required packages to your document preamble:
% \usepackage{multirow}
\begin{table}[htb]
\centering
\caption{Performance for Subtask 2a in Validation Set }
\label{tab:Performance for Subtask 2a}
\resizebox{\columnwidth}{!}{%
    \begin{tabular}{cclll}
    \hline
    \multirow{2}{*}{\textbf{Models}}  & \multirow{2}{*}{\textbf{Strategy}} & \multicolumn{3}{c}{\textbf{Metrics}}                                     \\ \cline{3-5} 
                                                                                         &                   & \multicolumn{1}{l}{\textbf{H-F1}}  & \multicolumn{1}{l}{\textbf{H-Prec}} & \textbf{H-Rec} \\ \hline
    Baseline                                                                             & Official          & \multicolumn{1}{l}{0.4589} & \multicolumn{1}{l}{0.6820}  & 0.3457 \\ \hline
    \multirow{3}{*}{\makecell[c]{CLIP + \\ XLM-R Large}}         & GC                & \multicolumn{1}{l}{0.5214} & \multicolumn{1}{l}{0.6320}  & 0.5775 \\ 
                                                                                         & Stacking   + LCL  & \multicolumn{1}{l}{0.6265} & \multicolumn{1}{l}{0.6343}  & 0.5947 \\  
                                                                                         & Stack   + GC      & \multicolumn{1}{l}{0.6675} & \multicolumn{1}{l}{0.7598}  & 0.6107 \\ \hline
    \multirow{3}{*}{\makecell[c]{CLIP + \\ XLM-R-Large Twitter }} & GC                & \multicolumn{1}{l}{0.5581} & \multicolumn{1}{l}{0.6369}  & 0.6103 \\ 
                                                                                         & Stacking   + LCL  & \multicolumn{1}{l}{0.6567} & \multicolumn{1}{l}{0.6459}  & 0.6178 \\  
                                                                                         & Stacking   + GC   & \multicolumn{1}{l}{\textbf{\underline{0.7134}}} & \multicolumn{1}{l}{\textbf{\underline{0.7652}}}  & \textbf{\underline{0.6418}} \\ \hline
    \end{tabular} %
}
\end{table}

% Please add the following required packages to your document preamble:
% \usepackage{multirow}
% \usepackage[table,xcdraw]{xcolor}
% Beamer presentation requires \usepackage{colortbl} instead of \usepackage[table,xcdraw]{xcolor}
\begin{table}[htb]
\centering
\caption{Performance for Subtask 2b in Validation Set}
\label{tab:Performance for Subtask 2b}
\resizebox{\columnwidth}{!}{%
    \begin{tabular}{cccc}
    \hline
    \multirow{2}{*}{\textbf{Models}} & \multirow{2}{*}{\textbf{Strategy}} & \multicolumn{2}{c}{\textbf{Metrics}}                 \\ \cline{3-4} 
                            &                           & \multicolumn{1}{c}{\textbf{F1   macro}} & \textbf{F1   micro} \\ \hline
    Baseline                & Official                  & \multicolumn{1}{c}{0.2500}      & 0.3333      \\ \hline
    \makecell[c]{CLIP + \\ XLM-R-Large} & Binary Classification     & \multicolumn{1}{c}{0.7618} & 0.7947      \\ \hline
    \makecell[c]{CLIP + \\ XLM-R-Large Twitter} & Binary Classification           & \multicolumn{1}{c}{\textbf{\underline{0.8023}}}      & \textbf{\underline{0.8216}}      \\ \hline
    \end{tabular} %
}
\end{table}

\paragraph{Results} Our study's evaluation of the official validation set showcases the impactful of domain knowledge in enhancing model performance for persuasive technique identification across various subtasks. \textbf{Subtask 1}: As shown in Table \ref{tab:Performance for Subtask 1}, the model fine-tuned with domain knowledge performs significantly better in identifying persuasive techniques. Among the three classification strategies, Stack + GC demonstrates superior performance compared to the other two strategies. \textbf{Subtask 2a}: The evaluation results for this task, as depicted in Table \ref{tab:Performance for Subtask 2a}, align with Subtask 1. The model fine-tuned with domain knowledge outperforms others in identifying persuasive techniques. Among the three classification strategies, Stack + GC exhibits superior performance. \textbf{Subtask 2b}: In Table \ref{tab:Performance for Subtask 2b}, this task involves binary classification. We utilized a multimodal model for binary classification and achieved competitive scores through external data augmentation methods.

%我們在官方提供有標籤的 validation set上進行評估。\textbf{subtask 1} : 由 table \ref{tab:Performance for Subtask 1} 可以看到有經過領域知識微調過模型在識別說服力技巧上效能明顯比較好。在3個分類策略，Stack+GC的效能明顯高於其他兩種策略。\textbf{subtask 2a} : 由 table \ref{tab:Performance for Subtask 2a} 此任務的評估結果與subtask 1一致，有經過領域知識微調過模型在識別說服力技巧上效能明顯比較好。在3個分類策略，Stack+GC的效能明顯高於其他兩種策略。\textbf{subtask 2b} : 由 table \ref{tab:Performance for Subtask 2b} 這是一個二元分類任務，我們使用多模態模型進行二元分類，透過外部資料增強的方式，獲得具有競爭力的分數。





% Please add the following required packages to your document preamble:
% \usepackage{multirow}
% \usepackage[normalem]{ulem}
% \useunder{\uline}{\ul}{}
\begin{table}[htb]
\centering
\caption{Performance for Subtask 1 in Test Set}
\label{tab:Performance for Subtask 1 test}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{clccc}
\hline
\multirow{2}{*}{\textbf{Languages}}        & \multirow{2}{*}{\textbf{Method}} & \multicolumn{3}{c}{\textbf{Metries}}                                \\ \cline{3-5} 
 &             & \textbf{H-F1}      & \textbf{H-Prec}     & \textbf{H-Rec}        \\\hline
\multirow{2}{*}{English}          
& Baseline          & 0.36865                & 0.47711                & 0.30036                \\
& Ours              & \textbf{\underline{0.66271}} & \textbf{\underline{0.60990}} & \textbf{\underline{0.72552}}   \\\hline
\multirow{2}{*}{Bulgarian}        
& Baseline          & 0.28377                & 0.31881                & 0.25567                \\
& Ours              & \textbf{\underline{0.51744}}                & \textbf{\underline{0.53578}}                & \textbf{\underline{0.50031}}                \\\hline
\multirow{2}{*}{North Macedonian}
& Baseline          & 0.30692                & 0.31403                & 0.30012                \\
& Ours              & \textbf{\underline{0.46165}}                & \textbf{\underline{0.54622}}                & \textbf{\underline{0.39975}}                \\\hline
\multirow{2}{*}{Arabic}           
& Baseline          & 0.35897                & 0.35000                & 0.36842                \\
& Ours              & \textbf{\underline{0.47500}}                & \textbf{\underline{0.42817}}                & \textbf{\underline{0.53333}}             \\ \hline
    \end{tabular} %
}
\end{table}

\begin{table}[htb]
\centering
\caption{Performance for Subtask 2a in Test Set}
\label{tab:Performance for Subtask 2a test}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{clccc}
\hline
\multirow{2}{*}{\textbf{Languages}}        & \multirow{2}{*}{\textbf{Method}} & \multicolumn{3}{c}{\textbf{Metries}}                                \\ \cline{3-5} 
 &             & \textbf{H-F1}      & \textbf{H-Prec}     & \textbf{H-Rec}        \\\hline
\multirow{2}{*}{English}          
& Baseline          & 0.44706                & 0.68778                & 0.33116                \\
& Ours              & \textbf{\underline{0.70677}} & \textbf{\underline{0.78164}} & \textbf{\underline{0.64498}}   \\\hline
\multirow{2}{*}{Bulgarian}        
& Baseline          & 0.50000                & \textbf{\underline{0.80428}}                & 0.36276                \\
& Ours              & \textbf{\underline{0.54864}}                & 0.70691                & \textbf{\underline{0.44828}}                \\\hline
\multirow{2}{*}{North Macedonian} 
& Baseline          & \textbf{\underline{0.55525}}                & \textbf{\underline{0.90219}}                & \textbf{\underline{0.40103}}                \\
& Ours              & 0.48707                & 0.70575                & 0.37185                \\\hline
\multirow{2}{*}{Arabic}           
& Baseline          & \textbf{\underline{0.48649}}                & \textbf{\underline{0.65000}}                & 0.38870                \\
& Ours              & 0.48323                & 0.59466                & \textbf{\underline{0.40698}}             \\ \hline
    \end{tabular} %
}
\end{table}

\begin{table}[htb]
\centering
\caption{Performance for Subtask 2b in Test Set}
\label{tab:Performance for Subtask 2b test}
\resizebox{0.9\columnwidth}{!}{%
    \begin{tabular}{clccc}
    \hline
    \textbf{Languages}                & \textbf{Method}  & \textbf{F1 macro} & \textbf{F1 micro} \\ \hline
    \multirow{2}{*}{English}          & Baseline & 0.25000  & 0.33333  \\
                                      & Ours     & \textbf{\underline{0.78803}}  & \textbf{\underline{0.82167}}  \\\hline
    \multirow{2}{*}{Bulgarian}        & Baseline & 0.16667  & 0.20000  \\
                                      & Ours     & \textbf{\underline{0.64706}}  & \textbf{\underline{0.82000}}  \\\hline
    \multirow{2}{*}{North Macedonian} & Baseline & 0.09091  & 0.10000  \\
                                      & Ours     & \textbf{\underline{0.52000}}  & \textbf{\underline{0.79000}}  \\\hline
    \multirow{2}{*}{Arabic}           & Baseline & 0.22705  & 0.29375  \\
                                      & Ours     & \textbf{\underline{0.58518}}  & \textbf{\underline{0.59375}} \\  \hline

    \end{tabular} %
}
\end{table}

%在本研究中，我們提出了一種新穎的多維度階層分類（MDHC）策略，並在官方競賽的排行榜上獲得了具有競爭力的表現。如圖\ref{tab:Performance for Subtask 1 test}所示，我們的方法在Subtask 1中超越了所有官方基線，成功識別了四種不同語言的文字級迷因說服技巧，平均排名第四。這一成果證明了採用XLM-R-Large Twitter模型結合MDHC識別策略的有效性。

%然而，在多模態任務Subtask 2a中，我們的策略在英文迷因的識別上表現較好，但對於低資源語言如North Macedonian的表現不佳。我們分析認為，這可能是因為我們的方法主要針對訓練集中的英文迷因進行了預訓練，而對跨語言能力的提升有限。此外，不同語言背後的文化差異可能也對迷因的識別造成了影響。

%在Subtask 2b中，儘管我們的方法在細緻的跨語言迷因技巧識別上存在不足，但在識別包含特定說服技巧的文字與圖片結合的迷因方面，我們的策略仍然表現出競爭力，如表\ref{tab:Performance for Subtask 2b test}所示。

%總結來說，我們的研究在迷因說服技巧的識別任務上取得了一定的成果，但也暴露出了對低資源語言和文化差異處理的不足。未來的工作將致力於改進這些方面，以提高模型在多語言和多文化環境中的適應性和準確性。

In the test set of the competition, we propose a method that has demonstrated competitive performance on the official competition leaderboard. As shown in Table \ref{tab:Performance for Subtask 1 test}, our method outperforms all official baselines in Subtask 1, which involves the identification of meme persuasion techniques in four different languages at the text level. Our method ranks 4th on average across four languages (English, Bulgarian, North Macedonian, and Arabic), with rankings of 6th in English, 3rd in Bulgarian, 4th in North Macedonian, and 2nd in Arabic. These results indicating our method is competitive.

In the multimodal task, as shown in Table \ref{tab:Performance for Subtask 2a test}, We observe the performance of our method in Subtask 2a, where it demonstrates competitiveness in English memes. We attribute this to two main reasons. Firstly, our method only undergoes text-level domain pretraining on the English memes provided in the training dataset. As a result, it lacks the necessary representation capabilities for low-resource languages, such as North Macedonian. based on the above, our method does not improve cross-linguistic abilities; instead, it relies on the language representation capabilities of the multilingual model. Therefore, this makes it challenging to identify cross-lingual fine-grained persuasion techniques. Secondly, the inherent cultural differences in various languages may lead to discrepancies in the same set of memes presented in different languages, indicating a potential issue of cultural divergence.

Finally, in Subtask 2b, as shown in Table \ref{tab:Performance for Subtask 2b test}, although our proposed method falls short in the fine-grained cross-linguistic meme persuasion techniques identification, it remains competitive in tasks involving the identification of whether a meme, combining text and image, contains one of the persuasion techniques. Our method ranks 6th on average across four languages (English, Bulgarian, North Macedonian, and Arabic), with rankings of 7th in English, 5th in Bulgarian, 6th in North Macedonian, and 5th in Arabic. This demonstrates the competitive edge of our method in a multimodal context.

\section{Conclusion}
% 我們仔細的分析含有說服力技巧的階層圖，將其轉換成HMC任務，還探討了兩種 MDHC策略，並提出的長尾分佈的重要性以及採用DBL損失函數來緩解HMC帶來的長尾分佈問題。在模型方面我們建議利用領域內的預訓練來檢測含有說服性的Meme，在各項實驗顯示領域內訓練的有效性，同時在整個系統上名列前茅。
We conducted a detailed analysis of the HDAG containing persuasive techniques, transforming it into an HMC task. We also explored two MDHC strategies and highlighted the importance of addressing the long-tail distribution issue, proposing the use of the DBL loss function to mitigate this issue in HMC tasks. Regarding the models, we recommend utilizing domain-specific pre-training to detect memes containing persuasive elements and the effectiveness of domain-specific training was demonstrated across various experiments. Finally, we achieve competitive results in the competition.



% Bibliography entries for the entire Anthology, followed by custom entries
%\bibliography{anthology,custom}
% Custom bibliography entries only
\bibliography{custom}

\appendix

\clearpage

%\section{Implementation Details}
%During training, we use AdamW as the optimizer and an initial learning rate of 2e-5 for XLM-Roberta-twitter and 1e-4 for CLIP models.
%with a batch size of 32 and text max length set to 128 on subtask 1 and a batch size of 16, image size set to 224, and text max length set to 128 on subtask 2a and 2b. with all subtasks, the maximum number of epochs is set to 50. All experiments are conducted using two NVIDIA TITAN RTX GPUs  

\section{Implementation Details} \label{apix:implementation_details}
During training, we use AdamW as the optimizer and an initial learning rate of 2e-5 for XLM-Roberta-twitter and 1e-4 for CLIP models.
with a batch size of 32 and text max length set to 128 on subtask 1 and a batch size of 16, image size set to 224, and text max length set to 128 on subtask 2a and 2b. with all subtasks, the maximum number of epochs is set to 50. All experiments are conducted using two NVIDIA TITAN RTX GPUs 

\section{Data Distribution Details}
\subsection{Data Distribution of the Persuasion Techniques}
\begin{figure}[htb]
     \centering
     \setlength{\abovecaptionskip}{0.cm}
     \includegraphics[width=1\columnwidth]{latex/images/Ratio of the Persuasion Techniques for all subtask.jpg}
     \caption{Ratio of the Persuasion Techniques on Subtask 1,2a and 2b}
     \label{fig:Ratio_of_the_Persuasion_Techniques_for_all_subtask}
\end{figure}

\begin{figure}[bht]
     \centering
     \setlength{\abovecaptionskip}{0.cm}
     \includegraphics[width=0.9\columnwidth]{latex/images/Data distribution of P.T. in subtask 2a train set.png}
     \caption{Data Distribution of Persuasion Techniques on Subtask 2a Train set}
     \label{fig:Data_distribution_of_P.T._on_subtask_2a_train set}
\end{figure}

\subsection{ Data Distribution of MC for Persuasion Techniques in the MDHC strategy}
\begin{figure}[tb]
     \centering
     \setlength{\abovecaptionskip}{0.cm}
     \includegraphics[width=0.7\columnwidth]{latex/images/Data distribution of MC for P.T. of Ethos in the MDHC strategy.jpg}
     \caption{Data Distribution of MC for Persuasion Techniques of Ethos in the MDHC Strategy on Subtask 2a}
     \label{fig:Data_distribution_of_MC_for_P.T._of_Ethos_in_the_MDHC_strategy}
\end{figure}
\begin{figure}[tb]
     \centering
     \setlength{\abovecaptionskip}{0.cm}
     \includegraphics[width=0.7\columnwidth]{latex/images/Data distribution of MC for P.T. of Logos in the MDHC strategy.jpg}
     \caption{Data Distribution of MC for Persuasion Techniques of Logos in the MDHC Strategy on Subtask 2a}
     \label{fig:Data_distribution_of_MC_for_P.T._of_Logos_in_the_MDHC_strategy}
\end{figure}
\begin{figure}[tb]
     \centering
     \setlength{\abovecaptionskip}{0.cm}
     \includegraphics[width=0.7\columnwidth]{latex/images/Data distribution of MC for P.T. of Pathos and Logos in the MDHC strategy.jpg}
     \caption{Data Distribution of MC for Persuasion Techniques of Pathos and Logos in the MDHC Strategy on Subtask 2a}
     \label{fig:Data_distribution_of_MC_for_P.T._of_Pathos_and_Logos_in_the_MDHC_strategy}
\end{figure}



\end{CJK*}
\end{document}
