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\title{BAMBAS at SemEval-2024 Task 4: How far can we get without looking at hierarchies?}

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\author{\bf Arthur B. Vasconcelos$^{1}$, Luiz Matos$^{1}$, Eduardo Corr\^{e}a Gon\c{c}alves$^{2}$, \\ \bf Eduardo Bezerra$^{3}$, Aline Paes$^{1}$ and Alexandre Plastino$^{1}$ \\
    $^1$ Institute of Computing, Universidade Federal Fluminense, Niter\'oi, RJ, Brazil \\
        $^2$ National School of Statistical Sciences (ENCE/IBGE), RJ, Brazil \\
        $^3$ Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ), RJ, Brazil\\
        \{\textit{athurbittencourt,lfmatosmelo}\}\textit{@id.uff.br}, \\
        \textit{eduardo.correa@ibge.gov.br}, \textit{ebezerra@cefet-rj.br}, 
        \{\textit{alinepaes,plastino}\}\textit{@ic.uff.br}}

\begin{document}
\maketitle
\begin{abstract}
This paper describes the BAMBAS team's participation in SemEval-2024 Task 4 Subtask 1, which focused on the multilabel classification of persuasion techniques in the textual content of Internet memes. We explored a lightweight approach that does not consider the hierarchy of labels. First, we get the text embeddings leveraging the multilingual tweets-based language model, Bernice. Next, we use those embeddings to train a separate binary classifier for each label, adopting independent oversampling strategies in each model in a binary-relevance style. We tested our approach over the English dataset, exceeding the baseline by 21 percentage points, while ranking in 23\textsuperscript{th} in terms of hierarchical F1 and 11\textsuperscript{st} in terms of hierarchical recall.
\end{abstract}

\section{Introduction}

In the multilabel classification problem (MLC), each instance may belong to zero, one, or multiple class labels. The goal is to learn 
a system to infer the correct labels of previously unseen 
instances~\citep{gfp:18, mml:23}. MLC has several real-world applications, ranging from text categorization \citep{shimura-etal-2018-hft} to 
protein and gene function prediction~\citep{cbc:12}. This work addresses a critical novel application of MLC: detecting persuasion techniques in memes, considering only their textual content, a subtask of SemEval-2024 task4\footnote{\url{https://propaganda.math.unipd.it/semeval2024task4/}}. 

The Merriam-Webster dictionary\footnote{\url{https://www.merriam-webster.com/wordplay/meme-word-origins-history}} defines \textit{meme} as ``an amusing or 
interesting item (such as a captioned picture or video) or genre of items that 
is spread widely online, especially through social media''. Nonetheless, and 
unfortunately, in recent years, memes have been used not only to amuse 
people but also as a tool for disseminating disinformation in  
political campaigns~\citep{renee2018computational,doi:10.1080/17439884.2018.1544149}. Malicious actors embed sophisticated propaganda and persuasion techniques within these memes, employing psychological and rhetorical strategies. This manipulation extends to the memes' textual and visual components~\citep{dimitrov-etal-2021-detecting}.

Like other computational propaganda \citep{mcb:20}, memes significantly influence public opinion. Their effectiveness stems from their widespread reach, potentially impacting millions of internet users globally. Additionally, memes are often not perceived as propaganda by these users, primarily because they do not mirror the appearance of conventional political advertisements~\citep{nieubuurt2021internet}.

As an effort to address this problem, SemEval-2024 Task 4  \citep{semeval2024task4} promoted a 
challenge in which competitors should develop algorithms to identify 
the use of persuasion techniques in memes, considering only 
their textual content (Subtask 1) or text and image together 
(Subtasks 2a and 2b). In this paper, we describe our approach to addressing Subtask 1. 
For this subtask, the shared-task organizers made available a collection of 
8,500 texts in English extracted from real Internet memes
(7,000 for training and the remaining divided into validation 
and dev sets). Each text may be assigned to a set of labels that 
indicate the persuasion techniques present in it\footnote{Labels definitions are presented at \url{https://propaganda.math.unipd.it/semeval2024task4/definitions.html}}. There are a set of 20 possible labels organized in a hierarchy -- thus, we have a hierarchical multilabel classification problem \citep{cbc:12}. Some texts can have no label assigned, indicating they do not correspond to propaganda. 

The shared task aimed to produce the best model according to
the hierarchical-F1 metric. Test collections in four different languages were made available:  
English, Bulgarian, North Macedonian, and Arabic. Our team (BAMBAS) 
participated in the English challenge along with 31 other teams. 
We explored a lightweight approach based on three components. The first component is a language model from which we extract embedding features leveraging the \textsc{[CLS]} token. The second component is a binary relevance-based strategy to train 20 separate binary classifiers (one for each existing label)~\citep{bls:04}. Our central inquiry focused on assessing the extent to which such a lightweight model that does not engage with the intricacies of hierarchical structures could be effective. The third core component handles the inherent imbalance of multilabel hierarchical problems by employing an independent oversampling strategy \citep{cbl:02, mt:12} to reduce the imbalance between negative and positive examples present in each binary problem derived. 

The hierarchical-F1 score of our submitted solution exceeded the baseline by 21 percentage points. In the hierarchical F1-based rank, we were the 23\textsuperscript{th} out of 31 teams. However, when considering the hierarchical recall, we were ranked as 11\textsuperscript{st} 
\footnote{Our code and experiments are available at \url{https://github.com/MeLLL-UFF/bambas}}.  

The rest of the paper is organized as follows. Section~\ref{sec:background} 
briefly overviews MLC concepts relevant to this paper. 
Section~\ref{sec:system} details our proposed system. In Sections \ref{sec:setup} and \ref{sec:results}, we present the experimental methodology and report the
results, respectively. Finally, Section~\ref{sec:conclusions} brings the conclusion and future research directions. 

\section{Background}
\label{sec:background}
Over the last 20 years, MLC  has been one of the most active research topics 
in machine learning~\citep{mml:23}. Among the several methods for multilabel
learning in the literature~\citep{btd:22, pkh:18}, Binary Relevance 
(BR)~\citep{bls:04} stands out as one of the most prominent methods. This approach decomposes the multilabel problem into $q$ binary problems, where $q$ is 
the number of labels. Then, one binary classifier is independently trained for each 
label. The labels of new instances are predicted by combining the outputs of each classifier.

The BR method offers several key advantages. Firstly, its simplicity and intuitiveness make it highly accessible. Additionally, BR models can predict label sets not present in the training set, owing to their composition as a series of independent binary classifiers. Most crucially, BR has consistently exhibited high prediction accuracy values across various domains. In a recent extensive experimental comparison~\citep{btd:22} 
involving 26 methods across 42 datasets, models utilizing BR 
outperformed all models trained with other different 
transformation strategies.

Nonetheless, the BR method suffers from three major drawbacks. 
First, it ignores the possible correlations among labels~\citep{zll:18}.
Second, BR has high training and prediction times for problems 
in which the number of labels is huge (tens of thousands
to millions)~\cite{pkh:18}.  Third, its predictive performance is affected by class imbalance, which occurs when the number of examples relevant to each label is much inferior to
the number of irrelevant ones~\citep{mml:23, zll:18}.

We consider that the first two drawbacks are not crucial for
addressing SemEval-2024 Task 4 Subtask 1, as the number of 
labels in the problem is not large ($q$ = 20) and
there is no strong correlation between any 
pair of labels in the training set. More specifically, we found that the highest Pearson correlation 
value is 0.13 -- between labels
``Glittering generalities (Virtue)'' and ``Flag-waving''. On the other hand, we consider that the issue of
class imbalance needs to be taken into account as the imbalance ratio (ratio of negative to positive
examples) is 47.38 on average in the training set, and the maximum value reaches 332.33 
 for the label ``Obfuscation, Intentional vagueness, Confusion''. 
Our approach is detailed in the next section.


\section{System overview}
\label{sec:system}
The shared task proposed in SemEval-2024 Task~4 comprises an output of one or more labels  -- in case the meme is a propaganda -- disposed in a hierarchical taxonomy of persuasion techniques. The root of such hierarchy is naturally labeled \textit{persuasion}, while the second level has three possible branches: \textit{ethos}, \textit{pathos}, \textit{logos}. While \textit{ethos} and \textit{logos} conduct to labels in a third level, 
\textit{pathos} branch connects directly to the persuasion techniques -- the leaves of the tree. This way, the final output can be one or more paths from the root to some leaf. 

Handling such a hierarchical structure directly is quite challenging in machine learning. The algorithms should accurately predict multiple outputs while respecting the labels' hierarchical relationships. However, errors can propagate down the hierarchy. Moreover, some paths have very few instances, adding another layer of complexity to the problem: data sparsity and imbalance. 

Therefore, our primary solution to the problem was to investigate how far an algorithm that disregards the hierarchy could go. Additionally, we also decided not to handle the multiple labels directly. However, employ the binary-relevance approach and consider a component to handle imbalance by adding synthetic instances with SMOTE~\citep{cbl:02} and RandomOverSampler~\citep{ROS:18}, for each binary problem. 

Algorithm~\ref{alg:train} depicts the training procedure and Algorithm~\ref{alg:inf} the inference. Our method hinges on three core components. The first one creates the features from the meme textual content, leveraging a pre-trained language model (line 3 in Algorithm~\ref{alg:train}). The second component addresses class imbalance by creating synthetic instances (line 10 in Algorithm~\ref{alg:train}). The third component trains independent binary classifiers (line 12 in Algorithm~\ref{alg:train}), employing the binary-relevance strategy. During the inference phase, each label classifier undergoes evaluation, and the instance is assigned all the positive classifications predicted by each classifier. 

\begin{algorithm}[!ht]
    \caption{Top-level Training Algorithm of BAMBAS team participation in SemEval-2024 Task4}
    \label{alg:train}
    \begin{algorithmic}[1] % The number tells where the line numbering should start
        % input: labels, dataset, smote rate, ptlm
            \State $feats \gets \emptyset$, $pos \gets \emptyset$, $neg \gets \emptyset, c_{labels} \gets \emptyset$
            \For{$meme \in dataset$} 
                \State $emb \gets ptlm(meme.text)$
                \State $feats.append(\textsc{cls} $ token from $emb$) \label{end_step1}
                \For{$label \in  meme.labels$}
                    \State $pos[label] \gets  pos[label]$ $\cup$ \par\hskip\algorithmicindent $meme.index$   
                \EndFor
                 \For{$label \not \in  meme.labels$}
                    \State $neg[label] \gets  neg[label]$ $\cup$ \par\hskip\algorithmicindent $meme.index$
                \EndFor
            \EndFor

            \For{$label \in labels$} 
                \State $aug\_pos[label], aug\_neg[label] \gets$ \par\hskip\algorithmicindent $oversampler(feats,pos[label],$ \par\hskip\algorithmicindent $neg[label], rate)$
            \EndFor

            \For{$label \in labels$}
                \State $c_{label} \gets train\_classifier(feats, $ 
                \par\hskip\algorithmicindent $ aug\_pos[label], aug\_neg[label])$
                \State $c_{labels} \gets c_{labels} \cup c_{label}$ 
            \EndFor
            \State {\textbf{return}} $c_{labels}$
    \end{algorithmic}
\end{algorithm}

\begin{algorithm}[!ht]
    \caption{Top-level Inference Algorithm of BAMBAS team}
    \label{alg:inf}
    \begin{algorithmic}[1] % The number tells where the line numbering should start
        % input: instance, classifiers, labels, ptlm
            \State $emb \gets ptlm(meme\_text)$
            \State $plabels \gets \emptyset$
            \For{$label \in  labels$}
                \State $plabel \gets c_{label}(emb)$
                \If{$plabel = True$}
                    \State $plabels \gets plabels \cup label$
                \EndIf
            \EndFor
            \State {\textbf{return}} $plabels$
    \end{algorithmic}
\end{algorithm}



\subsection{Extracting embedding from a pre-trained language model}\label{subsec:ptlm} In line with our straightforward premise, we have implemented a feature-based strategy that utilizes embeddings from pre-trained language models (PTLMs). The textual content of each meme is processed through the PTLM, allowing our system to capture the numeric feature vector from the \textsc{[CLS]} token. This method effectively harnesses the power of PTLMs to distill complex language information into a manageable form for further training our classifiers.

% \doubts{move to BK? or setup?}
Our selection choice for PTLMs includes a writing free-style multilingual model, namely, XLM-RoBERTa~\citep{conneau-etal-2020-unsupervised} and two informal writing style models, one monolingual (BERTweet~\citep{nguyen-etal-2020-bertweet}) and one multilingual (Bernice~\citep{delucia-etal-2022-bernice}). 

XLM-RoBERTa is a multilingual adaptation of the RoBERTa model, pre-trained on a 2.5TB of data across 100 languages. RoBERTa itself is a transformers model trained on large raw text corpora. The key training method used is Masked Language Modeling (MLM), where 15\% of the words in a sentence are masked, and the model predicts these masked words, learning a bidirectional representation of the sentence. BERTweet is a monolingual model trained from 850M Tweets. It has the same architecture as BERT-base but was trained using the RoBERTa pre-training procedure. Bernice is a multilingual RoBERTa language model trained from 2.5 billion tweets.

\subsection{Training classifiers for each class}\label{subsec:classifiers}
In our approach, we implemented the binary relevance strategy to train a suite of independent classifiers, each tailored to manage a binary prediction, of whether a meme belongs to a specific label. Our model comprises independent binary classifiers, each aligned to a distinct persuasion technique. Under this strategy, a classifier corresponding to a label $k$ is trained using a targeted approach: instances labeled with $k$ are treated as positive examples, while all other instances are considered negative. This selective process ensures that each classifier becomes specialized in precisely identifying its respective label.

For instance, consider a meme $m_j$ tagged with three labels ($k_1, k_2, k_3$). This meme serves as a positive training example for the classifiers $c_{k_1}$, $c_{k_2}$, and $c_{k_3}$, contributing to their ability to recognize these specific labels. Conversely, another meme $m_z$ tagged with label ($k_1$) not only acts as a positive example for training the classifier $c_{k_1}$ but also serves as a negative instance for $c_{k_2}$ and $c_{k_3}$. This dual role of memes in the training process, as both positive and negative examples depending on their label associations, underscores each classifier's nuanced and specialized training within our binary relevance framework.

During the inference phase, each meme is processed through all the classifiers in our system. If a particular classifier predicts the meme as a positive instance, the corresponding label is assigned to the meme. By the time this processing is finished, the input meme accumulates a set of labels, each representing a positive prediction from the respective binary classifiers. This method ensures that the meme is comprehensively evaluated for all potential labels. 

\subsection{Creating synthetic instances}\label{subsec:smote}
Considering the inherently imbalanced nature typical of multilabel hierarchical tasks~\citep{mml:23, zll:18}, we address this challenge by oversampling the dataset with synthetic instances. This approach is designed to equalize the number of examples for each binary classifier, thereby mitigating the imbalance issue. Our system generates synthetic examples independently for each binary classifier. 

We leveraged two strategies: a simple random oversampler and the widely-used SMOTE (Synthetic Minority Oversampling Technique)~\cite{cbl:02}. SMOTE operates by identifying examples that are closely situated in the feature space. It then generates a line connecting these examples and creates a new and synthetic sample at a point along this line.

More precisely, for each classifier $c_{k_i}$, the process starts by selecting a random example from the minority class. Next, it identifies $n$ nearest neighbors for this example. From these neighbors, one is randomly chosen. Subsequently, a synthetic example is crafted at a randomly determined point between the chosen neighbor and the original example in the feature space.


\section{Experimental setup}
\label{sec:setup}
Our solution was implemented using HuggingFace~\citep{wolf-etal-2020-transformers}\footnote{\url{https://huggingface.co/}} and scikit-learn~\citep{scikit-learn}\footnote{\url{https://scikit-learn.org/stable/}} libraries. The experiments were conducted on an NVidia DGX-1, using a single Tesla P100 GPU with 16GB of VRAM. We conducted a step-by-step analysis to reach the final modeling decisions. The intermediate results, necessary to decide the components of our final solution, are reported with the validation set. The final solution was trained with the training set and we report the results of the English dev and test set. We proceed this way because the dev set could only be measured with the submission page before the release of its gold labels.

%\add{datasets: can we add further info here? Most details will be presented in the task definition paper}

All results are reported with the competition's evaluation metric, a hierarchical variant of the F1-score~\citep{kiritchenko2006learning}. The metric considers the classification taxonomy, rewarding a full score for exact leaves prediction, and rewarding a partial score for ancestor predictions. The closer the predicted ancestor is to the correct labels, the higher the partial score. Additionally, we report the hierarchical variants of precision and recall.

The first analysis consists of defining the PTLM to extract the embeddings. We did not employ oversampling during this phase and applied a binary-relevance model using logistic regression. The PTLM and logistic regression hyperparameters were left as default. The meme textual content is presented to the PTLM without any pre-processing. Next, we explore 6 other classifiers besides logistic regression: decision tree, extra tree, extra trees, KNN, random forest, and ridge classifier. The last analysis focused on selecting the best oversampling strategy. We experimented with SMOTE and a random oversampling strategy, both implemented in the imbalanced-learn library\footnote{\url{https://imbalanced-learn.org/stable/}}. All the results so far included 20 binary classifiers, each associated with a persuasion technique in the leaves of the tree. Then, we investigate a final possibility of including some internal nodes related to the classes that were worst classified. The best model from those analyses was submitted to the competition.

\section{Results}
\label{sec:results}
Table~\ref{tab:ptlm} depicts the results achieved by each pre-trained language model mentioned in Section~\ref{subsec:ptlm} considering logistic regression and no oversampling strategy. Bernice achieves the best overall hierarchical-F1 (H-F1) results. We hypothesize that it was trained with a large set of informal texts from tweets, presenting a writing style close to those found in memes. Then, we select Bernice for the next analyses and to submit our final solution. 

\input{tables/ptlm.tex}

The next analysis concerns the method used as the base classifier of the binary relevance strategy. Table~\ref{tab:classifiers} depicts the results of the binary relevance when executed with each classifier. 
% achieved by each pre-trained language model mentioned in Section~\ref{sec:setup}. 
Logistic regression conducted to the best H-F1 score. Because of that, we proceed to the final analysis with it. 

%\add{table classifiers}
\input{tables/classifiers.tex}

Next, we explore our third core component, the oversampling technique. Table~\ref{tab:oversamplers} shows the results of running the random oversampler and SMOTE with 50/50 rate for oversampling, and also a hybrid version which combines the best oversampler for each binary classifier, using different oversampling rates: 0.1 to 1.0 with step of 0.1. 

%\add{rates}
\input{tables/oversamplers.tex}

Finally, we included additional classifiers to some internal nodes of the hierarchy. Such an extension includes only the internal nodes corresponding to the least accurately classified leaf nodes. These nodes are ``Ad Hominem'', ``Distraction'' and ``Logos''. Table~\ref{tab:internal_nodes_validation_set} shows the validation set results without and with the addition of those internal nodes. Recall improved with the combined strategy, while precision remained nearly identical.

\input{tables/internal_nodes_validation_set.tex}

Given the preceding results, we selected that approach for the final submission. Table~\ref{tab:final_results} shows the final results achieved by the solution we submitted to SemEval-2024 Task4 platform. In the first line, we highlight the results achieved on the dev set while the second line shows (in bold) the test set result. 

\input{tables/final_results.tex}


\section{Conclusion}
\label{sec:conclusions}
This paper addresses the SemEval-2024 competition with a lightweight solution to investigate how a model that neglects the hierarchy would behave in a hierarchical task. Our solution uses a tweets-based PTLM as a feature extractor, generates synthetic data to account for imbalance, and employs a binary relevance strategy to handle multiple labels. Our next step is to investigate training a structured output classifier that predicts the paths in the hierarchy. Moreover, given that oversampling strategies enhanced the performance of most of the classes, we plan to design other strategies explicitly tailored to the style of memes. 

\section*{Acknowledgments}
This research was financed by CNPq (National Council for Scientific and Technological Development), under grants 311275/2020-6 (Aline Paes) and 315750/2021-9 (Alexandre Plastino) and FAPERJ - \textit{Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro}, processes SEI-260003/000614/2023, SEI-260003/002930/2024 (Aline Paes) and E-26/201.139/2022 (Alexandre Plastino).


\bibliography{anthology,custom}

\appendix
\section{Validation set classification results per-label}
\label{sec:appendix}
Due to the imbalanced nature of the explored problem, we further investigate the classification results on a per-label basis. Table~\ref{tab:per_label_submission_results} describes the results for each label in the validation set. We include the internal nodes in the hierarchy alongside all leaves. To calculate scores for the internal nodes, predictions of any of their children are considered as correct node predictions. The best-performing label was ``Appeal to Authority'', which achieved the highest F1 score. The internal nodes ``Logos'' and ``Ad Hominem'' follows in second and third place, respectively. Also, most of the worst performing labels have scarce examples on the datasets, like ``Vagueness, Confusion'' and ``Straw Man''.

\input{tables/per_label_submission_results.tex}

\section{After competition deadline results: multilabel classifiers}

\input{tables/appendix_multilabel_dev_set.tex}
\input{tables/appendix_multilabel_test_set.tex}

The solution presented in the paper relaxes the multiple labels per example setting and trains independent binary classifiers for each class. We additionally explored an alternative setup that leverages a multi-layer perceptron (MLP) classifier with a multilabel classification layer. We trained two classifiers in this way: the first follows the previous feature-based approach to train a multilabel feedforward (FF) MLP; the second adds a multilabel classification layer on top of the PTLM and fine-tunes all its weights. As before, the PTLM is Bernice. 

The feature-based approach classifier includes a single 768-dimension hidden layer with scikit-learn default parameters. The fine-tuning  approach runs for five epochs, with a learning rate of $3.9e-5$ and weight decay of $1e-3$, all selected with the validation set. Both approaches did not involve oversampling, and the classifiers were trained with the union of the train and validation sets and evaluated on the dev set during training. 

Tables~\ref{tab:appendix_multilabel_dev_set} and~\ref{tab:appendix_multilabel_test_set} depict the results for the dev and test sets. The results show the superior performance of the fine-tuning approach, with test set H-F1 score higher than our official competition's submission. Also, the standalone FF classifier achieved F1 above average, indicating that a dedicated oversampling strategy for the multilabel approach is a promising research avenue to explore further in the future.



\end{document}
