On Compositional Generalization of Neural Machine Translation

• URL: http://arxiv.org/abs/2105.14802v1
• Date: Mon, 31 May 2021 09:04:29 GMT
• Title: On Compositional Generalization of Neural Machine Translation
• Authors: Yafu Li, Yongjing Yin, Yulong Chen and Yue Zhang
• Abstract summary: We study NMT models from the perspective of compositional generalization. We build a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization.
• Score: 11.171958188127961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.

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