# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 4 Results and Discussion
### Interpolation System Approach (ISA)
Table 12 shows that Ja-Id LM05 obtained higher perplexity scores than Ja-Id LM03, although Ja-Id LM05 obtained a higher BLEU score than Ja-Id LM03. We identified this higher perplexity score in Ja-Id using the target monolingual corpus size as the first parameter. The target monolingual corpus was trained by the LM toolkit, i.e., KenLM, and generated various lists of \(n\)-gram probabilities stored in _arpa_ file. We compared the English and Indonesian target monolingual corpus in Kk-En and Ja-Id. English, with a larger target monolingual size, i.e., 532,560 and longer LM order, i.e., LM05, had larger various lists of \(n\)-gram probabilities, i.e., 29,181,816. In contrast, Indonesian with a smaller target monolingual size, i.e., 8500 and longer LM order, i.e., LM05, had smaller various lists of \(n\)-gram probabilities, i.e., 740,766. As a result, the choice of language model probabilities in the decoding process could be smaller and affected the perplexity score of Ja-Id. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Language Pair**} & \multirow{2}{*}{**PT1**} & \multirow{2}{*}{**PT2**} & \multirow{2}{*}{**\#Same Phrase Pair**} & \multicolumn{6}{c}{**PT1**} & \multicolumn{6}{c}{**PT2**} \\ \cline{5-12}  & & & & **p(ts)** & **lex(ts)** & **p(st)** & **lex(st)** & **p(ts)** & **lex(ts)** & **p(st)** & **lex(st)** \\ \hline \multicolumn{12}{l}{Small phrase table} \\ \hline Kk–En LM03 & H-ISA & Std-ISA & 872 & 296 & **514** & **576** & **639** & **576** & 358 & 296 & 233 \\ Kk–En LM05 & H-ISA & Baseline & 708 & 131 & 342 & **412** & **474** & **577** & **366** & 296 & 234 \\ Ja–Id LM05 & Baseline & Std-ISA & 2000 & **1984** & **1975** & **1981** & **1964** & 16 & 25 & 19 & 36 \\ \hline \multicolumn{12}{l}{Large phrase} \\ \hline Ja–Id LM03 & Std-ISA & Baseline & 1825 & 13 & 18 & 17 & 34 & **1812** & **1807** & **1808** & **1791** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of phrase translation parameter scores between two phrase tables. Results in bold indicate the highest scores. Additionally, we identified another parameter that could have influenced the increase in perplexity scores in Ja-Id LM05: the feature function weight of LM. This parameter weight was generated from the target monolingual corpus and then stored in the decoder, moses.ini. A decoder is an SMT component that finds the best translation according to the product of the translation and language model probabilities [39]. A good value for a feature function weight of LM is 0.1-1. We found that the feature function weight of LM for Kk-En LM05 was higher than for Kk-En LM03 (0.10 and 0.06, respectively) when using a larger target monolingual corpus, i.e., 532,560. In contrast to Kk-En, we found that the feature function weight of LM for Ja-Id LM05 was lower, 0.09, than for Ja-Id LM03, 0.11, when using a smaller target monolingual corpus, i.e., 8500. As a result, Kk-En LM05 could have obtained a lower perplexity score than Kk-En LM03, whereas Ja-Id LM05 obtained a higher perplexity score than Ja-Id LM03, as shown in Table 12. Figure 2 shows the feature function weight of LM for Kk-En and Ja-Id. We evaluated the generated text of the systems. Tables 13 and 14 show two sentence examples in each language pair, marked by (1) and (2). Sentence (1) is a long sentence,  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Phrase-Pair**} & \multicolumn{1}{c}{**Phrase Translation Parameters**} & \multicolumn{1}{c}{**Scores**} \\ \cline{3-4}  & **Baseline** & **Std-ISE** & **H-ISA** \\ \hline \(\textit{h}\)\(\cdot\)\(  
whereas (2) is a short one. We added the English translation in the generated text of Ja-Id to better understand the translation results, marked in italics. Table 13 shows that the H-ISA generated a compact sentence compared to others in the short sentence. The compact sentence means that the generated text obtained the same keywords as a reference, i.e., without additional words.