Morphological Inflection Generation Using Character Sequence to Sequence Learning
Morphological Inflection Generation Using Character Sequence to Sequence Learning
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DOI：
10.18653/v1/n16-1077
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发表时间：
2015-12
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通讯作者：
Manaal Faruqui;Yulia Tsvetkov;Graham Neubig;Chris Dyer
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文献类型：
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作者：
Manaal Faruqui;Yulia Tsvetkov;Graham Neubig;Chris Dyer
词形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。我们将词形变化生成问题建模为字符序列到序列学习问题，并提出了神经编码器-解码器模型的变体来解决它。我们的模型是独立于语言的，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我们的系统，并获得与现有最先进的词形变化生成模型更好或相当的结果。
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.