Direct Neural Machine Translation with Task-level Mixture of Experts models

• URL: http://arxiv.org/abs/2310.12236v2
• Date: Sun, 19 May 2024 17:49:15 GMT
• Title: Direct Neural Machine Translation with Task-level Mixture of Experts models
• Authors: Isidora Chara Tourni, Subhajit Naskar,
• Abstract summary: Direct neural machine translation (direct NMT) translates text between two non-English languages. Task-level Mixture of expert models (Task-level MoE) has shown promising NMT performance for a large number of language pairs.
• Score: 1.2338729811609357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct neural machine translation (direct NMT) is a type of NMT system that translates text between two non-English languages. Direct NMT systems often face limitations due to the scarcity of parallel data between non-English language pairs. Several approaches have been proposed to address this limitation, such as multilingual NMT and pivot NMT (translation between two languages via English). Task-level Mixture of expert models (Task-level MoE), an inference-efficient variation of Transformer-based models, has shown promising NMT performance for a large number of language pairs. In Task-level MoE, different language groups can use different routing strategies to optimize cross-lingual learning and inference speed. In this work, we examine Task-level MoE's applicability in direct NMT and propose a series of high-performing training and evaluation configurations, through which Task-level MoE-based direct NMT systems outperform bilingual and pivot-based models for a large number of low and high-resource direct pairs, and translation directions. Our Task-level MoE with 16 experts outperforms bilingual NMT, Pivot NMT models for 7 language pairs, while pivot-based models still performed better in 9 pairs and directions.

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