论文标题
Sigmorphon 2020任务0和任务2的NYU-Cuboulder系统
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2
论文作者
论文摘要
我们描述了Sigmorphon 2020 Task 0的NYU-CUBOULDER系统在类型上多样化的形态学变化和无监督的形态学范式完成的任务2。前者包括产生来自引理的形态学弯曲以及描述靶形式的一组形态句法特征。后者需要仅从原始文本中为一组给定的引理产生整个范例。我们将形态学的变形模拟为序列到序列问题，其中输入是带有形态标签的引理特征的序列，而输出是变形形式特征的序列。首先，我们将变压器模型应用于任务。其次，随着弹性形式与引理共享大多数字符，我们进一步提出了一个指针生成器变压器模型，以轻松复制输入字符。我们对任务0的最佳性能系统将在23个系统中排名第六。我们进一步将拐点系统用作任务2方法的子组件。任务2的最佳性能系统是7个提交中最好的。
We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form. The latter requires generating entire paradigms for a set of given lemmas from raw text alone. We model morphological inflection as a sequence-to-sequence problem, where the input is the sequence of the lemma's characters with morphological tags, and the output is the sequence of the inflected form's characters. First, we apply a transformer model to the task. Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters. Our best performing system for Task 0 is placed 6th out of 23 systems. We further use our inflection systems as subcomponents of approaches for Task 2. Our best performing system for Task 2 is the 2nd best out of 7 submissions.
