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The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

机译:SIGMORPHON 2020任务0和任务2的NYU-CUBoulder系统

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We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on ty-pologically 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.
机译:我们将针对SIGMORPHON 2020任务0的类型-形态学多样的形变和任务2的无监督形态范式完成描述NYU-CUBoulder系统。前者包括从一个引理和一组描述目标形式的形态句法特征生成形态学变化。后者需要仅从原始文本为一组给定引理生成整个范式。我们将形态学变形建模为序列到序列的问题,其中输入是带有形态学标记的引理字符的序列,而输出是变形形式的字符的序列。首先,我们将变压器模型应用于任务。其次,由于变形形式与引理共享大多数字符,因此我们进一步提出了一种指针生成器转换模型,以允许轻松复制输入字符。我们针对任务0表现最好的系统在23个系统中排名第6。我们还将拐点系统用作任务2方法的子组件。我们在任务2中表现最好的系统是7项提交中的第二好的系统。

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