# Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages
## 2 Related Work  
### Phrase Table Combination  
Phrase table combination has been used to improve the translation quality in direct translation and pivot approaches. In direct translation, researchers have combined two or more phrase tables of direct translation with various symmetrizations of word alignment [9; 12]. [PERSON] and [PERSON] [12] used two approaches in their direct translation of Spanish-English: the direct translation of single symmetrization and phrase table combination. The first approach used three symmetrizations: gdfand, grow-diag, and intersection. The second approach combined three phrase tables produced by gdfand, grow-diag, and intersection. Their experimental results showed that phrase table combination outperformedthe direct translation of a single symmetrization approach. [PERSON] [9] explored massive experiments, also arriving at the same conclusion. They combined three to eight phrase tables of direct translation in the Chinese-English language pair. The phrase tables were produced by several symmetrizations of word alignment: gdfand, intersection, union, grow-diag-final, grow-diag, grow, scrtotgt, tgttsrc, and grow-final. Their experimental results showed that the phrase table combination of several symmetrizations outperformed the Baseline. Phrase table combination could also be used in pivot approaches to overcome the data scarcity between src-trg language pairs [1; 2; 3; 4; 6; 13; 14]. The phrase table combination outperformed direct translation or other pivot approaches, namely, sentence translation and triangulation. We identified that the phrase table combination in the pivot approaches uses the same symmetrization of word alignment, i.e., gdfand [1; 2; 3; 4; 6; 13; 14]. This is because the gdfand is a standard symmetrization of word alignment in statistical machine translation (SMT) [7]. Then, [PERSON] and [PERSON] [11] proposed the phrase table combination that combined two symmetrization of word alignments: grow-diag-final-and (GDFA) and grow-diag-final-and_Relaxation (GDFA_R), for the Hebrew-Arabic language pair. GDFA is a symmetrization of word alignment that starts with an intersection of two alignments. Then, the symmetrization adds alignment points between two unaligned words [8]. GDFA is a common symmetrization of word alignment used in word alignment tools such as GIZA++. GDF_R was constructed by [PERSON] and [PERSON] [11] using the following steps:  
1. Creating a list of all possible pivot unigrams using the intersections of the source-pivot and pivot-target corpora. 2. Building two-directional alignments model using grow-diag-final-and in pivot-target, namely, pivot-to-target \(\overrightarrow{A_{pt}}\) and target-to-pivot \(\overleftarrow{A_{pt}}\), subsequently combining the two-directional alignment models. Last, obtaining the final alignment metrics of a new phrase obtained by removing a given word unlisted in the pivot unigram. This second part was also applied to source-pivot. The system combination of the GDFA and GDF_R symmetrizations obtained an absolute improvement of 0.8 BLEU points compared to GDFA_R, which verified the superiority of the phrase table combination. From those studies, the application of the phrase table combination could improve the translation quality. Nevertheless, the symmetrization of word alignment can vary across language pairs and datasets (language- and dataset-specific). [PERSON] et al. [8] explored five symmetrizations (final, final-and, grow-diag, grow, and intersection) in five language pairs (Arabic-English, Japanese-English, Korean-English, Chinese-English, and English-Chinese). Given an example of Japanese-English, they obtained the highest BLEU score of 45.1 when using intersection, and the lowest BLEU score of 39.0 when using grow-diag. [PERSON] et al. [10] explored five symmetrizations (intersection, grow-diag, grow-diag-final-and, grow-diag-final, and union) in German-English.