Morphological Inflection Generation Using Character Sequence to Sequence Learning

机译：使用字符序列进行序列学习的形态学变形生成

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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.

机译：形态学变形生成是生成与特定语言转换相对应的给定引理的变形形式的任务。我们将拐点生成问题建模为字符序列，以对序列学习问题进行建模，并提出了用于解决该问题的神经编码器-解码器模型的变体。我们的模型与语言无关，可以在监督和半监督环境中进行训练。我们在形态丰富的语言的七个数据集上评估了我们的系统，并获得了与现有最新屈折产生模型相比较更好的结果或可比的结果。

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《Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies》|2016年|634-643|共10页
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Manaal Faruqui; Yulia Tsvetkov; Graham Neubig; Chris Dyer;
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4. Morphological Inflection Generation Using Character Sequence to Sequence Learning [C] . Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 2016

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机译：利用字符序列进行形态学变形生成学习

Morphological Inflection Generation Using Character Sequence to Sequence Learning

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