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IIT(BHU)-IIITH at CoNLL-SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection

机译:IIT(BHU)-IIITH在CoNLL-SIGMORPHON 2018上共同完成了通用形态再造的任务

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This paper describes the systems submitted by IIT (BHU), Varanasi/IIIT Hyderabad (IITBHU-IIITH) for Task 1 of CoNLL-SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection (Cotterell et al., 2018). The task is to generate the inflected form given a lemma and set of morphological features. The systems are evaluated on over 100 distinct languages and three different resource settings (low, medium and high). We formulate the task as a sequence to sequence learning problem. As most of the characters in inflected form are copied from the lemma, we use Pointer-Generator Network (See et al., 2017) which makes it easier for the system to copy characters from the lemma. Pointer-Generator Network also helps in dealing with out-of-vocabulary characters during inference. Our best performing system stood 4th among 28 systems, 3rd among 23 systems and 4th among 23 systems for the low, medium and high resource setting respectively.
机译:本文介绍了IIT(BHU),瓦拉纳西/海得拉巴(IIIT)海得拉巴(IITBHU-IIITH)为CoNLL-SIGMORPHON 2018通用形态再思考共同任务(Cotterell等人,2018)提交的任务1提交的系统。任务是在给定引理和一组形态学特征的情况下生成变形的形式。系统使用100多种不同的语言和三种不同的资源设置(低,中和高)对系统进行了评估。我们将任务表述为序列学习问题的序列。由于大多数变形形式的字符都是从引理中复制的,因此我们使用了Pointer-Generator Network(参见et al。,2017),这使得系统更容易从引理中复制字符。指针生成器网络还有助于在推理过程中处理语音不足的字符。我们的最佳系统在低,中和高资源设置方面分别位于28个系统中的第4位,23个系统中的第3位和23个系统中的第4位。

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