<\/p>\n\n\n\n
Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language\u2019s representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs.<\/p>\n","protected":false},"excerpt":{"rendered":"
Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language\u2019s representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Surangika Ranathungaa","user_id":0},{"type":"text","value":"Shravan Nayak","user_id":0},{"type":"text","value":"Shih-Ting Cindy Huang","user_id":0},{"type":"text","value":"Yanke Mao","user_id":0},{"type":"text","value":"Tong Su","user_id":0},{"type":"text","value":"Yun-Hsiang Ray Chan","user_id":0},{"type":"text","value":"Songchen Yuan","user_id":0},{"type":"text","value":"Anthony Rinaldi","user_id":0},{"type":"text","value":"Annie Lee","user_id":0}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"ACM Trans. Asian Low Resour. Lang. Inf. 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