论文标题
可解释模式在形态深度学习中的作用
The Role of Interpretable Patterns in Deep Learning for Morphology
论文作者
论文摘要
我们研究了字符模式在三个任务中的作用：形态分析，诱饵和复制。我们使用标准序列到序列模型的修改版本，其中编码器是模式匹配网络。每个模式在源侧分数所有可能的n个字符长子词（子字），而评分子字的得分最高来初始化解码器以及注意机制的输入。此方法允许学习输入的哪些子词对于生成输出很重要。通过在同一源但目标上训练模型，我们可以比较哪些子词对于不同的任务以及它们之间的关系很重要。我们定义了一个相似性度量，这是JACCARD相似性的广义形式，并为在同一源上工作但目标可能有所不同的三个任务中的每个任务分配了相似性得分。我们检查了这三个任务如何用12种语言相互关联。我们的代码公开可用。
We examine the role of character patterns in three tasks: morphological analysis, lemmatization and copy. We use a modified version of the standard sequence-to-sequence model, where the encoder is a pattern matching network. Each pattern scores all possible N character long subwords (substrings) on the source side, and the highest scoring subword's score is used to initialize the decoder as well as the input to the attention mechanism. This method allows learning which subwords of the input are important for generating the output. By training the models on the same source but different target, we can compare what subwords are important for different tasks and how they relate to each other. We define a similarity metric, a generalized form of the Jaccard similarity, and assign a similarity score to each pair of the three tasks that work on the same source but may differ in target. We examine how these three tasks are related to each other in 12 languages. Our code is publicly available.
