多任务序列预测突尼斯阿拉伯语多级标注
Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level Annotation
摘要：
在本文中，我们提出了一个多任务序列预测系统，基于递归神经网络和多层次的阿拉伯突尼斯语料库注释。进行的注释是文本分类，标记，PoS标记和编码突尼斯阿拉伯语到CODA* 阿拉伯语正字法。该系统学习从Arabizi输入开始级联预测所有注释级别。我们在TIGER德语语料库上评估了该系统，适当地将数据转换为多任务问题，以显示我们的神经架构的有效性。我们还展示了我们如何使用该系统来注释突尼斯Arabizi语料库，该语料库后来被手动纠正并用于进一步评估突尼斯数据的序列模型。我们的系统是为Fairseq框架开发的，它允许快速简便地用于任何其他序列预测问题。
英文摘要：
In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotation performed are text classification, tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic orthography. The system is learned to predict all the annotation levels in cascade, starting from Arabizi input. We evaluate the system on the TIGER German corpus, suitably converting data to have a multi-task problem, in order to show the effectiveness of our neural architecture. We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data. Our system is developed for the Fairseq framework, which allows for a fast and easy use for any other sequence prediction problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
