Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yor\`ub\'a which uses bare nouns but marks these features contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yor\`ub\'a into English. We investigate how the systems what extent they identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperforms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT outperforms the NMT models for 1 category.

本研究利用语言对比的细粒度分析方法，比较了三种机器翻译模型在汉英翻译中的表现。结果显示，针对使用裸名词的约鲁巴语文本，Transformer模型表现最好，SMT和BiLSTM在不同分类上各有优劣，与人类翻译模式相比。这些结果对于低资源情况下的模型表现评估提供了一定的启示。

低资源、形态标记无法处理的环境下的Yoruba-English机器翻译