Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Thuy-Trang Vu, Shahram Khadivi, Dinh Phung, Gholamreza Haffari · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
Generalising to unseen domains is underexplored and remains a challenge in neural machine translation.Inspired by recent research in parameter-efficient transfer learning from pretrained models, this paper proposes a fusionbased generalisation method that learns to combine domain-specific parameters.We propose a leave-one-domain-out training strategy to avoid information leaking to address the challenge of not knowing the test domain during training time.Empirical results on three language pairs show that our proposed fusion method outperforms other baselines up to +0.8 BLEU score on average.