Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results extend previous findings that character representations are effective across typologies, and we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most others. But we also find room for improvement: none of the character-level models match the predictive accuracy of a model with access to true morphological analyses, even when learned from an order of magnitude more data.

通过系统地比较不同的基本表示单元、表示单元的组合方式和建模的语言的形态学类型，我们发现用 bi-LSTM 组合字符三元组表示法在大多数情况下优于其他方法，但是没有任何一种基于字符级别的模型能够与直接使用真实的形态学分析相比拟，即使学习数据增加一个数量级。

从字符到单词之间: 我们是否捕捉到了形态学?