# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 1 Introduction  
Low-resource languages suffer from data scarcity, which leads to poor translation quality. One of the common techniques to improve translation quality is using a phrase table combination in pivot approaches [1; 2; 3; 4; 5; 6]. The phrase table or translation model comes from a word alignment model that uses a symmetrization technique to generate phrase pairs. The standard symmetrization for word alignment model is grow-diag-final-and (gdfand) [7]. Although prior studies have shown that non-standard symmetrization, i.e., intersection, could obtain higher bilingual evaluation understudy (BLEU) scores than the standard one [8; 9; 10], non-standard symmetrization has not been commonly used in pivot approaches. Thus, the appropriate symmetrization of word alignment model needs to be investigated to improve the performance of low-resource languages when using phrase table combination in pivot approaches. [PERSON] and [PERSON] [11] studied phrase table combinations based on the symmetrization of word alignment model in pivot approaches for the Hebrew-Arabic language pair. Their proposed approach was based on symmetrization relaxation, which extracted newphrase pairs by removing a given word that was unlisted in the pivot phrase tables. They constructed two new symmetrizations, union_Relaxation (U_R) and grow-diag-final-and_Relaxation (GDFA_R), based on union and gdfand, respectively. The BLEU score from the combination of the GDFA and GDFA_R symmetrizations was 0.8 higher than that of the GDFA_R, which demonstrated the superiority of the phrase table combination. Unlike [PERSON] and [PERSON] [11], in this study, we propose a strategy, i.e., phrase table combination, that uses symmetrization of word alignment, which obtains the highest BLEU scores. Our strategy is based on previous research results that showed that symmetrization of word alignment is language-specific [8; 9; 10] and dataset-specific [12], as different language pairs with different datasets have different BLEU scores. In contrast to [PERSON] and [PERSON] [11], who removed a given word, our strategy employs available parallel corpora of source-target (src-trg), source-pivot (src-pvt), and pivot-target (pvt-trg) without removing the phrase pair that could be a potential candidate in the translation process. We applied our proposed strategy in pivot approaches to deal with two low-resource language pairs: Kazakh-English (Kk-En) and Japanese-Indonesian (Ja-Id). Kk-En and Ja-Id are considered low-resource language pairs because of the scarcity of their parallel corpora. In this study, we used Russian and Malaysian as pivot languages for Kk-En and Ja-Id, respectively. Our experimental results demonstrate that our proposed strategy obtains higher BLEU scores than the direct translation and system of standard symmetrization in Kk-En and Ja-Id. In short, this study provides several contributions as follows:  
* We explore two language model (LM) orders, 3-gram and 5-gram, and five symmetrizations, gdfand, intersection, union, srctqgt, and tgttsrc, in a direct system approach (DSA). We find that 5-gram is a better choice for Kk-En and Ja-Id. We also find that non-standard symmetrization, i.e., tgttsrc, could improve the BLEU score compared to the standard one, i.e., gdfand. * We propose a strategy in an interpolation system approach (ISA), named highest-ISA (H-ISA). The H-ISA uses the symmetrization of the word alignment that obtained the highest BLEU scores from the DSA for each phrase table of src-trg, src-pvt, and pvt-trg. We find that the H-ISA could be a competitive approach because it outperforms direct translation in Kk-En with absolute improvements of 0.35 and 0.22 BLEU points for 3-gram and 5-gram, respectively. Our proposed strategy also outperforms the direct translation of 3-gram in Ja-Id with an absolute improvement of 0.11 BLEU point.