# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 5 Conclusions and Future Work  
We investigated the effect of the symmetrization of word alignment on the translation quality of Kk-En and Ja-Id language pairs in pivot approaches. First, we explored five symmetrization techniques, gdfand, intersection, union, srctotgt, and tgttsrc, in two LM orders, 3-gram and 5-gram, in the direct system approach (DSA). We found that non-standard symmetrization, i.e., tgttsrc, obtained a higher BLEU score than the standard one, i.e., gdfand. We identified that despite using the same automatic word alignment, i.e., GIZA++, the phrase translation parameter score of tgttsrc was higher than that of gdfand. Thus, the BLEU scores of tgttsrc could be much higher than those of gdfand. Additionally, we found that the longer LM order, i.e., 5-gram, obtained a higher BLEU score than the shorter one (3-gram). Second, we proposed an approach to phrase table combination called H-ISA. The H-ISA is our interpolation system that uses the symmetrization of word alignment that obtained the highest BLEU scores from the DSA for each phrase table of src-trg, src-pvt, and pvt-trg. Our H-ISA is a competitive approach because it outperformed the direct translation of src-trg and standard-ISA (Std-ISA) by 0.38 and 0.22 in Kk-En LM03 and LM05, respectively. The H-ISA also outperformed the direct translation of src-trg by 0.11 in Ja-Id LM03. The direct translation of src-trg still outperformed the H-ISA by 0.12 in Ja-Id LM05. We found that the phrase translation parameter score of H-ISA in Ja-Id was  
\begin{table}
\begin{tabular}{l l} \hline \hline  & \multicolumn{1}{c}{(I)} \\ Source & \(  
lower compared to that of baseline. We also found that the small phrase table could obtain higher BLEU scores, which contradicts the findings of a previous study [11]. Third, we evaluated the perplexity score of the ISA system. We found that the longer LM order, i.e., 5-gram, obtained lower perplexity scores than the shorter one, (3-gram) in Kk-En. However, the results for Ja-Id were the opposite to Kk-En. We found that this result could be caused by two parameters: the target monolingual corpus size and the feature function weight of the LM. For the first parameter, we identified that the English target monolingual corpus size in Kk-En was 62 times bigger, i.e., 532,560, than the Indonesian target monolingual corpus size in Ja-Id, i.e., 8500. Thus, the English LM obtained 29,181,816 various lists of \(n\)-gram probabilities, whereas the Indonesian LM obtained 740,766. We argued that smaller datasets could have limited the choice of LM probabilities in the decoding process and affected the perplexity scores of Ja-Id. For the second parameter, Ja-Id LM05 obtained a lower feature function weight of LM, i.e., 0.09, than Ja-Id LM03, i.e., 0.11. As a result, the perplexity scores of Ja-Id LM05 were higher than those of Ja-Id LM03. We argued that the lower feature function weight of LM could obtain lower translation score in the decoding process. Lastly, we evaluated the generated text from both Kk-En and Ja-Id language pairs. In the short sentence, the H-ISA could produce a more compact keyword compared to direct translation of src-trg and Std-ISA. However, the generated text of H-ISA had an incorrect word order. In the long sentence, the incorrect word order was more severe, resulting in ambiguous sentences in the target language. We found that the generated text tended to follow the source language's sentence pattern, i.e., SOV, but the sentence pattern of the target language is SVO. When we compared the perplexity scores of Ja-Id to those of Kk-En, we found that the target monolingual corpus size could be a factor for decreasing the perplexity scores. Thus, we will increase our Indonesian target monolingual corpus size in Ja-Id to be as large as Kk-En. Then, we will re-evaluate this parameter in our next work. Regarding the incorrect word order problem, we will implement the pre-ordering technique in the training dataset to improve the generated text and re-evaluate our system in our future work.