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docs(papers): cite the remaining named data sources and baselines #91

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Binary file modified docs/paper-arabic/main.pdf
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3 changes: 2 additions & 1 deletion docs/paper-arabic/main.tex
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Expand Up @@ -110,7 +110,7 @@ \section{Related Work}
\section{Data}
\subsection{Sources}
\begin{itemize}
\item \textbf{Tashkeela-full}: the complete 75M-word classical dump,
\item \textbf{Tashkeela-full}~\cite{tashkeela}: the complete 75M-word classical dump,
cleaned with a Sadeed-style pipeline (orthographic normalization,
haraqat consistency checks, length filtering).
\item \textbf{Arabic Wikipedia}: modern prose, distilled of
Expand Down Expand Up @@ -323,6 +323,7 @@ \section{Reproducibility}
\bibitem{mtp} Gloeckle et al. Better \& Faster Large Language Models via Multi-token Prediction. arXiv:2404.19737, 2024.
\bibitem{gkd} Agarwal et al. On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR, 2024. arXiv:2306.13649.
\bibitem{gtpo} Tan et al. GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy. arXiv:2508.04349, 2025.
\bibitem{tashkeela} Taha Zerrouki and Amar Balla. Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems. \emph{Data in Brief}, 11:147--151, 2017.
\end{thebibliography}

\end{document}
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8 changes: 6 additions & 2 deletions docs/paper-umbrella/main.tex
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Expand Up @@ -72,9 +72,9 @@ \section{System and results}
Arabic & diacritization & \textbf{2.29/1.33\% DER} & Sadeed~\cite{sadeed} 7.29/5.26\%, Gemini 3.19/2.38\%, GLM-5.3-Flash 8.80 (same benchmark+evaluator) \\
Hebrew & diacritization & \textbf{16.43\% DER} (Biblical) & DictaBERT~\cite{dictabert} 35.6\% (same test) \\
Thai & G2P & \textbf{2.32\% PER} & baseline 6.37\% (same test) \\
Persian & G2P/HA & \textbf{89.5\%} / 77.3\% (SB) & Homo-GE2PE 76.9\% \\
Persian & G2P/HA & \textbf{89.5\%} / 77.3\% (SB) & Homo-GE2PE~\cite{homoge2pe} 76.9\% \\
Persian & diacritization & \textbf{0.52\% CER} & (none existed) \\
Urdu & G2P & \textbf{14.77\% CER} & epitran 60.0\% \\
Urdu & G2P & \textbf{14.77\% CER} & epitran~\cite{epitran} 60.0\% \\
Urdu & diacritization & \textbf{3.74\% CER} & (none existed) \\
\bottomrule
\end{tabular}
Expand Down Expand Up @@ -156,6 +156,10 @@ \section{Reproducibility}
\bibitem{byt5} Xue et al. ByT5: Towards a token-free future with
pre-trained byte-to-byte models. TACL, 2022. arXiv:2105.13626.
\bibitem{dictabert} DictaBERT-large-char-menaked. Dicta, 2023--2024.
\bibitem{homoge2pe} Qharabagh et al. Rethinking G2P with Rich Data and
Rule-Based Models. arXiv:2505.12973, 2025.
\bibitem{epitran} David R. Mortensen, Siddharth Dalmia, and Patrick
Littell. Epitran: Precision G2P for Many Languages. \emph{LREC}, 2018.
\end{thebibliography}

\end{document}