The paper presents four models submitted to Part 2 of the SIGMORPHON 2021 Shared Task 0, which aims at replicating human judgements on the inflection of nonce lexemes. Our goal is to explore the usefulness of combining pre-compiled analogical patterns with an encoder-decoder architecture. Two models are designed using such patterns either in the input or the output of the network. Two extra models controlled for the role of raw similarity of nonce inflected forms to existing inflected forms in the same paradigm cell, and the role of the type frequency of analogical patterns. Our strategy is entirely endogenous in the sense that the models appealing solely to the data provided by the SIGMORPHON organisers, without using external resources. Our model 2 ranks second among all submitted systems, suggesting that the inclusion of analogical patterns in the network architecture is useful in mimicking speakers' predictions.

本文研究了结合预编译的类比模式和编码器-解码器架构的技术，旨在在SIGMORPHON组织提供的数据上验证其预测非单词的屈折形式的效果，结果表明，在网络架构中加入类比模式有助于模仿说话者的预测。

尚未达到：将类比模式与编码器-解码器网络相结合，实现认知合理的屈折变化