Hybrid Statistical Machine Translation for E nglish- M yanmar: UTYCC Submission to WAT -2021 (2021)

Abstract: In this paper we describe our submissions to WAT-2021 (Nakazawa et al., 2021) for English-to-Myanmar language (Burmese) task. Our team, ID: “YCC-MT1”, focused on bringing transliteration knowledge to the decoder without changing the model. We manually extracted the transliteration word/phrase pairs from the ALT corpus and applying XML markup feature of Moses decoder (i.e. -xml-input exclusive, -xml-input inclusive). We demonstrate that hybrid translation technique can significantly improve (around 6 BLEU scores) the baseline of three well-known “Phrase-based SMT”, “Operation Sequence Model” and “Hierarchical Phrase-based SMT”. Moreover, this simple hybrid method achieved the second highest results among the submitted MT systems for English-to-Myanmar WAT2021 translation share task according to BLEU (Papineni et al., 2002) and AMFM scores (Banchs et al., 2015).

## introduction while both statistical machine translation (smt) and neural machine translation (nmt) have proven successful for high resource language, it is still an open research question how to make it work well especially for the low resource and long distance reordering language pairs such as english and burmese @xcite , @xcite , @xcite , @xcite . to the best of our knowledge there are only two publicly available english-myanmar parallel corpora; alt corpus @xcite and ucsy corpus @xcite for research purpose, and the size of the corpora are around 20k and 200k respectively. the parallel data for myanmar-english machine translation share task at workshop on asian translation (wat) using combination of that two corpora and thus it is a good chance for the nlp researchers who are working on low resource machine translation. motivated by this challenge, we represented the university of technology, yatanarpon cyber city (utycc) and participated in the english-myanmar (en-my) share task of wat2021 @xcite . in this paper, we propose one hybrid system based on plugging xml markup translation knowledge to the smt decoder. the translation rules for transliteration and borrowed words, and direct usage of english words in the target language are constructed by using a parallel word dictionary. the english-myanmar transliteration dictionary was built by manual extracting parallel words/phrases from the whole alt corpus. this simple hybrid method outperformed the three baselines and achieved the second highest results among the submitted mt systems for englishto-myanmar wat2021 translation share task according to bleu @xcite and amfm scores @xcite . the remainder of this paper is organized as follows. in section 2, we introduce the data preprocessing, including word segmentation and cleaning steps. in section 3, we describe the details of our three smt systems. the machine translation evaluation metrics are presented in section 4. the manual extraction process of transliteration word/phrase pairs from the alt english-myanmar parallel data is described in section 5. then, the smt decoding with xml markup technique is described in section 6. in section 7, we present hybrid translation results achieved by all our systems. section 8 concludes this paper. ## smt systems in this section, we describe the methodology used in the machine translation experiments for this share task. ## evaluation our systems are evaluated on the alt test set and we used the different evaluation metrics such as bilingual evaluation understudy (bleu) @xcite , rankbased intuitive bilingual evaluation score (ribes) @xcite , and adequacy-fluency metrics (amfm) @xcite . for the official evaluation of english-to-myanmar share task, we uploaded our hypothesis files to the wat2021 evaluation server and sub-syllable (almost same with sylbreak toolkit's syllable units) segmentation was used for myanmar language. we submitted "hybrid pbsmt with xml markup (inclusive)" and "hybrid osm with xml markup (inclusive)" systems training only with ucsy corpus for human evaluation. ## manual extraction of parallel transliteration words when we studied on the myanmar language corpus provided by the wat2021, we found that many sentences are very long, containing spelling errors, unnaturalness of translation (i.e. translation from english to myanmar) and many transliteration words (i.e. wordby-word, phrase-by-phrase, compound word transliteration). moreover, the alt corpus was extracted from the english wikinews @xcite and it contains many named entity words such as person names, organizations, locations. see three example english-myanmar parallel sentences of the alt test data that contained several transliteration words. this paper tackling the problem of machine translation on transliteration word/phrase pairs by hybrid translation approach. we manually extracted english-myanmar transliteration word and phrase pairs from the whole alt corpus and prepared 14,225 unique word dictionary 1 . the main categories are country/city names, demonyms, personal names, month names, general nouns, organization names, abbreviations, units and english-to-english trans-1 https://github.com/ye-kyaw-thu/mtrss/tree/ master/wat2021/en-my_transliteration-dict lation words (see ## hybrid translation generally, hybrid translation integrates the strengths of rationalism method and empiricist method. @xcite described how machine learning approaches can be used to improve the phrase substitution component of a hybrid machine translation system. essential of hybrid translation is to integrate the core of mt engines. multiple-engine hmt integrates all available mt methods, applying to their benefits, in order to improve qualities of output @xcite . the popular combinations comprise "rule-based machine translation vs the smt" and multiple combinations of machine translation engines, for example "smt vs neural machine translation". our work in this paper focuses on hybrid machine translation of smt engine and xml tags inserting (i.e. applying rules) into transliteration words of each source sentence. we used the moses smt toolkit and it also supports -xml-input flag to activate xml tags inserting feature with one of the five options; exclusive, inclusive, constraint, ignore and pass-through. refer manual page of the moses toolkit 2 for detail explanation. although we studied all options, we will present the two options that work well for english-myanmar hybrid translation. the moses decoder has an xml markup scheme that allows the specification of translations for parts of the sentence. in its simplest form, we can guide the decoder what to use to translate certain transliteration words or phrases in the source sentence. we wrote a perl script for xml markup inserting into the source english sentences based on the manually extracted transliteration dictionary. as shown in follows, the xml markup scheme for hpbsmt is different with pbsmt and osm. this is because the syntactic annotation of the hpbsmt system also used xml markup. and thus, we used --xml-brackets "{{ }}" option when decoding hybrid hpb-smt system. ## results our systems are evaluated on the alt test set and the results are shown in ## conclusion we presented in this paper the utycc's participation in the wat-2021 shared translation task. our hybrid smt submission to the task performed the second in english-to-myanmar translation direction according to several evaluation scores including the de facto bleu. our results also confirmed the xml markup technique for transliteration words dramatically increase the translation performance up to 6 bleu scores. moreover, our results highlighted that hybrid statistical machine translation is easy to implement and we need to explore more for low-resource distant language pairs such as english-myanmar translation.