This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the inflected form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperform the next best competitor.

本篇论文介绍了苏黎世大学在SIGMORPHON 2017共享任务中针对形态重构的提交。 我们专注于神经网络方法，可以在有限资源的情况下解决任务。 我们提出了两种具有硬单调注意机制的循环神经网络架构，这些架构在复制方面很强大，并且在实现方面存在显着差异。 随后，我们提出了一些实验，通过字符对齐技术使翻译连贯，并基于样本量100的条件下，所提出的两种方法（以模型集合的形式）均优于下一竞争对手，成为了SIGMORPHON 2017共享任务1的全局胜者。

Align and Copy: UZH 在 SIGMORPHON 2017 共享任务中的形态回缀表现