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\title{Team Name at SemEval-2024 Task 2: Descriptive Title}

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  \texttt{email@domain} \\\And
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  Affiliation / Address line 2 \\
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  \texttt{email@domain} \\}

\begin{document}
\maketitle
\begin{abstract}
    \begin{itemize}
        \item short description of the task
        \item summary of your approach
        \item summary of findings/contributions
        \item links to code if available
    \end{itemize}
\end{abstract}

\section{Introduction}
\begin{itemize}
\item What is the task about and why is it important? Be sure to mention the language(s) covered and you must cite the task overview paper (\cite{jullien-etal-2024-semeval}). ~1 paragraph

\item What is the main strategy your system uses? ~1 paragraph

\item What did you discover by participating in this task? Key quantitative and qualitative results, such as how you ranked relative to other teams and what your system struggles with. ~1 paragraph
\end{itemize}

\section{Background}
\begin{itemize}
    \item In your own words, summarize important details about the task setup: kind of input and output (give an example if possible); what datasets were used, including language, genre, and size. If there were multiple tracks, say which you participated in.
    \item Here or in other sections, cite related work that will help the reader to understand your contribution and what aspects of it are novel.
    \item Please, consider including the following references in your system description paper \cite{jullien-etal-2023-nli4ct,jullien-etal-2023-semeval}.
\end{itemize}

\section{System overview}
\begin{itemize}
    \item Key algorithms and modeling decisions in your system; resources used beyond the provided training data; challenging aspects of the task and how your system addresses them. This may require multiple pages and several subsections, and should allow the reader to mostly re-implement your system’s algorithms.
    
    \item Use equations and pseudocode if they help convey your original design decisions, as well as explaining them in English. If you are using a widely popular model/algorithm like logistic regression, an LSTM, or stochastic gradient descent, a citation will suffice—you do not need to spell out all the mathematical details.
    
    \item Give an example if possible to describe concretely the stages of your algorithm.

    \item describe the reasoning for choices you have made in the design of your approach wrt the Semeval task.

    \item If you have multiple systems/configurations, delineate them clearly.

\item This is likely to be the longest section of your paper.
\end{itemize}



\section{Experimental setup}
\begin{itemize}
\item Key details about preprocessing, hyperparameter tuning, etc. that a reader would need to know to replicate your experiments. If space is limited, some of the details can go in an Appendix.

\item How data splits (train/dev/test) are used.

\item External tools/libraries used, preferably with version number and URL in a footnote.

\item Summarize the evaluation measures used in the task.

\item You do not need to devote much—if any—space to discussing the organization of your code or file formats.
\end{itemize}

\section{Results}
\begin{itemize}
\item Main quantitative findings: How well did your system perform at the task according to official metrics? How does it rank in the competition, for which metrics.

\item Quantitative analysis: Ablations or other comparisons of different design decisions to better understand what works best. Indicate which data split is used for the analyses (e.g. in table captions). If you modify your system subsequent to the official submission, clearly indicate which results are from the modified system.

\item Error analysis: Look at some of your system predictions to get a feel for the kinds of mistakes it makes. compare across the interventions
\end{itemize}


\begin{table}[t]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{@{}lccc@{}}
\toprule
\textbf{Model} & \textbf{Base F1} & \textbf{Consistency} & \textbf{Faithfulness} \\
\midrule
Model 1 & 0.64 & 0.67 & 0.82 \\
Model 2 & [Placeholder] & [Placeholder] & [Placeholder] \\
Model 3 & [Placeholder] & [Placeholder] & [Placeholder] \\
% Add more models as needed
\bottomrule
\end{tabular}%
}
\caption{Basic table template 1}
\label{table:metrics}
\end{table}

\begin{table}[t]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{Model} & \textbf{Base F1} & \textbf{Contrast F1} & \textbf{Consistency} & \textbf{Faithfulness} \\
\midrule
Model 1 & 0.64 & 0.45 & 0.67 & 0.82 \\
Model 2 & [Placeholder] & [Placeholder] & [Placeholder] & [Placeholder] \\
Model 3 & [Placeholder] & [Placeholder] & [Placeholder] & [Placeholder] \\
% Add more models as needed
\bottomrule
\end{tabular}%
}
\caption{Basic table template 2}
\label{table:metrics_contrast}
\end{table}


\section{Conclusion}

A few summary sentences about your system, results, and ideas for future work.


\section{Acknowledgments}
Anyone you wish to thank who is not an author, which may include grants and anonymous reviewers.

\bibliography{custom}

\appendix

\section{Example Appendix}
\label{sec:appendix}

Any low-level implementation details—rules and pre-/post-processing steps, features, hyperparameters, etc.—that would help the reader to replicate your system and experiments, but are not necessary to understand major design points of the system and experiments.

Any figures or results that aren’t crucial to the main points in your paper but might help an interested reader delve deeper.
\end{document}
