\begin{table}[h]
    \centering
    \begin{tabular}{llll}
        \hline
        \textbf{Model} & \textbf{Dev} & \textbf{Test} & \textbf{Ranking}\\
        \hline
        Baseline & 3.53 & 21.54 & 14\\
        \hline
        Character-level* & 8.35 & 45.83 & 28\\
        Word2vec* & 7.02 & 38.35 & 27\\
        Joint model & 6.36 & 34.88 & 25\\
    \end{tabular}
    \caption{Results for SemEval-2024 Task 8, subtask C. Dev and Test columns report mean absolute error (MAE) on the respective data partitions. The ranking column refers to the model ranking in the shared task competition. The scores and ranking of the unofficial submissions were not provided by the organisers and computed by us. There was a total of 33 submissions.\\ \textbf{*} unofficial submissions}
    \label{tab:c_results}
\end{table}

Table \ref{tab:c_results} outlines our results for subtask C. In this subtask, we were unable to match the transformer baseline.

Our official submission, the joint model, achieved a mean standard error of 6.36 on the development set, falling short of around 3 points from the baseline provided by the organisers. The difference is even more dramatic when it comes to the test set, where the gap widens to around 13 points. This is also far off from the best performing solutions in the shared task, which achieved a MAE of 15.7.

The character and Word2vec models failed to outperformed both the baseline and the joint model for the development set, and this remains the case in the test set. This reinforces the idea that extracting as much information as possible from the texts is key to performance in this subtask.

Overall, the models developed for subtask C have proven to be somewhat unrefined. The test set seems to be particularly punishing towards solutions that do not generalize well, but the results, while highlighting the shortcomings of our models, also point toward the potential that these approaches can have, with more attention dedicated to them.