BriefGPT.xyz
May, 2019
形态变化的语境化
Contextualization of Morphological Inflection
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Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, Trevor Cohn, Jason Eisner
TL;DR
本文介绍了一种利用神经混合图模型构建并预测词形变化的方法,并将其与传统的形态学变化或表面实现进行了比较,证明了将语言学驱动的潜在变量纳入NLP模型的实用性。
Abstract
Critical to
natural language generation
is the production of correctly inflected text. In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. Unlike traditional morphological
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