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    <abstract>Despite the tremendous success of Neural Machine Translation (NMT), its performance on low- resource language pairs still remains subpar, partly due to the limited ability to handle previously unseen inputs, i.e., generalization. In this paper, we propose a method called Joint Dropout, that addresses the challenge of low-resource neural machine translation by substituting phrases with variables, resulting in significant enhancement of compositionality, which is a key aspect of generalization. We observe a substantial improvement in translation quality for language pairs with minimal resources, as seen in BLEU and Direct Assessment scores. Furthermore, we conduct an error analysis, and find Joint Dropout to also enhance generalizability of low-resource NMT in terms of robustness and adaptability across different domains.</abstract>
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%0 Conference Proceedings
%T Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables
%A Araabi, Ali
%A Niculae, Vlad
%A Monz, Christof
%Y Utiyama, Masao
%Y Wang, Rui
%S Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F araabi-etal-2023-joint
%X Despite the tremendous success of Neural Machine Translation (NMT), its performance on low- resource language pairs still remains subpar, partly due to the limited ability to handle previously unseen inputs, i.e., generalization. In this paper, we propose a method called Joint Dropout, that addresses the challenge of low-resource neural machine translation by substituting phrases with variables, resulting in significant enhancement of compositionality, which is a key aspect of generalization. We observe a substantial improvement in translation quality for language pairs with minimal resources, as seen in BLEU and Direct Assessment scores. Furthermore, we conduct an error analysis, and find Joint Dropout to also enhance generalizability of low-resource NMT in terms of robustness and adaptability across different domains.
%U https://aclanthology.org/2023.mtsummit-research.2
%P 12-25

Markdown (Informal)

[Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables](https://aclanthology.org/2023.mtsummit-research.2) (Araabi et al., MTSummit 2023)

• Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables (Araabi et al., MTSummit 2023)

ACL

• Ali Araabi, Vlad Niculae, and Christof Monz. 2023. Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 12–25, Macau SAR, China. Asia-Pacific Association for Machine Translation.