Align和Copy：UZH在SIGMORPHON 2017上的共同任务是形态重构
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
摘要：
本文介绍了苏黎世大学提交的SIGMORPHON 2017共享任务的形态反射。任务是预测给定的词元和一组形态句法特征的屈折形式。我们专注于神经网络的方法，可以在有限的资源设置处理任务。由于将引理转换为变形形式主要是通过复制引理字符，因此我们提出了两种具有硬单调注意力的递归神经网络架构，它们在复制方面很强，但在实现这一点方面有很大的不同。第一种方法是具有复制机制的编码器-解码器模型。第二种方法是一个神经状态转换系统在一组显式的编辑动作，包括指定的复制动作。我们对字符对齐进行了实验，发现天真、贪婪的对齐在某些语言中始终会产生很强的效果。我们的最佳系统组合是SIGMORPHON 2017共享任务1的总冠军，无需外部资源。在具有100个训练样本的设置下，我们的两种方法作为模型的集合，都优于下一个最好的竞争对手。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
