# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 2 Related Work
### Kk-En and Ja-Id as Low-Resource Language Pairs  
Kk-En and Ja-Id are considered low-resource language pairs due to the scarcity of available parallel corpora. The available parallel corpora of Kk-En are open source parallel corpus (OPUS) [15] and news-commentary, which have 953,240 parallel sentences in total. Similarly, the available parallel corpora of Ja-Id are Asian language treebank (ALT) [16], TUFS Asian language parallel corpus (TALPCo) [17], and OPUS [15], which contain 1,468,155 parallel sentences in total. Due to this limitation, we propose an alternative approach to deal with the low-resource language pairs Kk-En and Ja-Id.  
The morphological segmentation approach produced a progressive improvement in the translation quality of Kk-En in the SMT model [18; 19]. The morphological segmentation is an approach that breaks words into morphemes. The approach was implemented because Kazakh is considered an agglutinative and highly inflected language. Figure 1 shows Kazakh words without morphological segmentation. Word \"\(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\  
systems that use transfer learning: Charles University (CUNI) [21], National Institute of Information and Communications Technology (NICT) [22], and University of Maryland (UMD) systems [23]. The NICT and CUNI systems use Russian-English as the parent model and obtained BLEU scores of 26.2 and 18.5, respectively. In comparison with both systems, the UMD system uses Turkish-English as the parent model and obtained a BLEU score of 9.2. Their experimental results [21; 22; 23] showed that the third language was still needed to improve the Kk-En translation quality.  
Unlike Kk-En, the SMT model still outperformed NMT with an absolute improvement of 3.93 BLEU points in Ja-Id [24]. Moreover, the pivot approaches of the SMT model obtained a higher BLEU score than direct translation in Ja-Id [4; 25]. [PERSON] et al. [25] showed that the single pivot approach produced an absolute gain of up to 0.23 BLEU points compared to direct translation. [PERSON] and [PERSON] [4] showed that single and multiple pivot approaches obtained absolute improvements of 0.31 and 0.41 BLEU points, respectively, compared to direct translation. In this study, we focused on phrase table combination in the single pivot approach of the SMT model.  
In addition to the model and techniques, [PERSON] and [PERSON] [26] and [PERSON] and [PERSON] [27] found several morphological issues in Ja-Id: word order problems, incorrectly defined phrases, and words with affixes. In particular, [PERSON] and [PERSON] [26] proposed several techniques: using pos-tag, increasing the LM dataset, stemming for the Indonesian dataset, removing Japanese particles for the Japanese dataset, and removing the named entity (NE). The BLEU score with the Japanese particles removal outperformed the baseline by 0.02. Then, [PERSON] and [PERSON] [27] proposed several techniques: the pos-tag model, the hierarchical model, lemmatizer, and post-processing. The pos-tag model and lemmatizer outperformed the baseline by 0.1 BLEU points. However, [PERSON] and [PERSON] [26] and [PERSON] and [PERSON] [27] did not show their proposed generated text results. Consequently, it is hard to compare the pre- and post-proposed generated texts. In this paper, we show the generated texts to investigate whether morphological issues are still encountered when implementing the pivot approaches.