We present a neural model for morphological inflection generation which
employs a hard attention mechanism, inspired by the nearly-monotonic alignment
commonly found between the characters in a word and the characters in its
inflection. We evaluate the model on three previously studied morphological
inflection generation datasets and show that it provides state of the art
results in various setups compared to previous neural and non-neural
approaches. Finally we present an analysis of the continuous representations
learned by both the hard and soft attention \cite{bahdanauCB14} models for the
task, shedding some light on the features such models extract.

提出了一种神经模型，可用于形态学词形变化生成，该模型采用硬注意机制，基于单词中的字符与其变形中字符之间通常发现的近单调对齐。在三个以前研究过的形态学词形变化生成数据集上评估了该模型，并展示了它相对于以前的神经和非神经方法在各种设置下提供的最新结果。最后，对该机制的硬和软注意力模型学习到的连续表示进行了分析，从而阐明了这些模型提取的特征。

硬性单调注意力下的形态变化生成

Morphological Inflection Generation with Hard Monotonic Attention

Morphological inflection generation is the task of generating the inflected
form of a given lemma corresponding to a particular linguistic transformation.
We model the problem of inflection generation as a character sequence to
sequence learning problem and present a variant of the neural encoder-decoder
model for solving it. Our model is language independent and can be trained in
both supervised and semi-supervised settings. We evaluate our system on seven
datasets of morphologically rich languages and achieve either better or
comparable results to existing state-of-the-art models of inflection
generation.

本研究提出了一种基于神经编码器解码器模型的方法，用于生成 Morphological inflection generation 任务中给定基本形式的词汇对应的特定语言变换的变形形式，并在 7 个形态丰富语言的数据集上进行评估，获得了与现有的最先进的基于变化生成的模型相比更好或可比的结果。