# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 4 Results and Discussion
### Direct System Approach (DSA)  
Table 3 shows the results of the DSA for each language model. Of 30 experiments, 20 showed that translation systems using LM05 produced higher BLEU scores than those using LM03. Table 3 also shows the BLEU scores of each symmetrization of word alignment. We found that the highest BLEU scores were not always generated by gdfand. For example, Kk-En LM05 tgttsrc obtained a higher BLEU score than Kk-En LM05 gdfand, that is, 3.56, showing that non-standard symmetrization could be an alternative option to improving the BLEU scores of the pivot approaches. Our results confirmed the language-specific [8; 9; 10] and dataset-specific [12] characteristics of the symmetrization of word alignment. We identified the reasons for the different BLEU scores in the same LM, despite using the same automatic word alignment and decoder weights, i.e., GIZA++ and moses.ini, respectively. We compared the phrase translation parameter scores between two phrase tables of the highest and second-highest BLEU scores in the same LM. Phrase translation parameter scores were computed from the co-occurrence of aligned phrases in the training corpora, then stored in the phrase table along with the phrase pair. The scores consisted of inverse phrase translation probability (\(p(t|s)\)), inverse lexical weighting (\(lex(t|s)\)), direct phrase translation probability (\(p(s|t)\)), and direct lexical weight (\(lex(s|t)\)). First, we collected 2000 phrase pairs and their phrase translation parameter scores from the phrase table with the highest BLEU score: phrase Table 1 (PT1). Subsequently, we collected 12,000 phrase pairs and their phrase translation parameter scores from phrase table with the second-highest BLEU score: phrase Table 2 (PT2). Last, we examined which component of the phrase translation parameter of PT1 obtained higher scores than the phrase translation parameter of PT2 in the same phrase pair, as shown in Table 4. We changed PT2 from srctqgt to tgttsrc since the results could not obtain the same phrase pairs in two language pairs, Kk-Ru and Ru-En, marked by an asterisk (*) in Table 4. The comparison algorithm of phrase translation parameter scores between the two phrase tables can be accessed in our repositories [37]. Table 4 shows that most language pairs obtained higher score in \(p(t|s)\) and \(lex(t|s)\), except for two language pairs: Kk-En and Ms-Id. The inverse phrase translation probability (\(p(t|s)\)) and inverse lexical weighting (\(lex(t|s)\)) were obtained from the target-source (\(t|s\)) parallel corpora. The results indicated that target-source (\(t|s\)) parallel corpora more strongly influence the phrase translation parameter score than source-target (\(s|t\)) parallel corpora. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Language Pair** & **PT1** & **PT2** & \(p(t|s)\) & \(lex(t|s)\) & \(p(s|t)\) & \(lex(s|t)\) \\ \hline Kk–En & tgttsrc & gdfand & 37 & 68 & **71** & **89** \\ Kk–Ru * & gdfand & tgttsrc & **68** & **65** & 4 & 38 \\ Ru–En * & gdfand & tgttsrc & **33** & **34** & 0 & 34 \\ Ja–Id & gdfand & tgttsrc & **338** & **269** & 49 & 101 \\ Ja–Ms & gdfand & tgttsrc & **390** & **300** & 39 & 119 \\ Ms–Id & tgttsrc & gdfand & 18 & 894 & **88** & **1060** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison result of phrase translation parameter scores between two phrase tables. Results in bold indicate the highest scores. PT2 was changed from srctqgt to tgttsrc, marked by an asterisk (*).