No Domain Left behind (2022)

Abstract: We participated in the WMT General MT task and focus on four high resource language pairs: English to Chinese, Chinese to English, English to Japanese and Japanese to English). The submitted systems (LanguageX) focus on data cleaning, data selection, data mixing and TM-augmented NMT. Rules and multilingual language model are used for data filtering and data selection. In the automatic evaluation, our best submitted English to Chinese system achieved 54.3 BLEU score and 63.8 COMET score, which is the highest among all the submissions.

## introduction training neural machine translation models for a specific domain is a well-studied task. however, maximizing the performance of a single nmt model for multiple domains remains difficult. as a former translator and a current machine translation engineer, i always dream about building a versatile machine translation systemno domain left behind. our neural machine translation system is developed using big transformer @xcite architecture and the toolkit i used is fairseq @xcite . rules, multilingual language model and faiss @xcite are used to align, clean and select parallel data. the following techniques are used in model training: a. data mixing is used to mix general domain corpus with specific domain corpus; b. back translation @xcite is not applied because it is time-consuming. instead, neural machine translation with monolingual translation memory @xcite ., 1 http://mteval.cipsc.org.cn:81/agreement/description 2021) is used to fully utilize the monolingual corpus. ## data filtering and selection the chinese-english parallel data is mainly from ccmt corpus 1 , inhouse domain data from translation projects, as well as parallel data aligned from multilingual websites and e-books. the monolingual data for multiple domains is collected from the internet and e-books. wmt newstest2021 is used to evaluate the model's general domain performance. multiple domainspecific test sets are created to evaluate the model's specific domain performance. each domain has a test set of 1,000 sentences. in order to build a versatile machine translation system, a total of 15 domains are covered in preparing the parallel and monolingual corpus. the primary domains and subdomains are listed as follows: literature web novel famous literary work literature/poetry idioms/maxims/sayings slang conversation names (personal, company) symbols / abbreviations / acronyms art, history and philosophy arts/crafts/painting cooking/culinary/gastronomy folklore history philosophy graphic arts/photo/imaging music religion no domain left behind hui zeng languagex ai lab felix_zeng_ai@aliyun.com social science, sociology, ethics, etc. economy, finance and business business/commerce accounting finance (general) investment / securities insurance economics real estate fashion and marketing advertising / public relations marketing / market research cosmetics / beauty fashion textiles / clothing / fashion clothing/textiles politics and national defense government/politics international org/dev/coop military / defense law law (general) law: contract(s) law: patents/trademarks/copyrights law: taxation & customs computers and it (information technology) computers (general) computers: systems, networks computers: hardware it (information technology) telecommunications internet, e-commerce sap system applications and products media / multimedia films and television cinema/film/tv/drama games, sports and entertainment games / video games / gaming / casino sports / fitness / recreation tourism & travel medical medical (general) medical: cardiology medical: dentistry medical: health care medical: instruments medical: pharmaceuticals dentistry veterinary genetics nutrition industry and engineering engineering nuclear eng/sci automation & robotics automotive / cars & trucks mechanics / mech engineering construction / civil engineering transport / transportation / shipping electronics / elect eng petroleum eng/sci surveying metallurgy / casting mining & minerals / gems energy / power generation maritime / sailing / ships industrial food/drink paper / paper manufacturing printing & publishing nuclear manufacturing furniture/household/appliance materials (plastics, ceramics, rubber, glass, wood etc.) science astronomy/space aerospace/aviation/space mathematics & statistics physics chemistry geography/geology architecture zoology biology botany meteorology metrology psychology education/pedagogy linguistics environment & ecology anthropology archaeology genealogy agriculture and animal husbandry agriculture fisheries forestry wood timber wood @xmath0 ## system description this section illustrates how the model is trained step by step. ## conclusion this paper describes languagex's translation system for the wmt2022 general mt task. the potential of a single translation model for all domains is explored. we are pleased to argue that, with data mixing and tm-augmented nmt, a versatile machine translation system with allround translation performance could be built.