在印度尼西亚使用Lstm网络的虚拟助手

Mirwan, Aryo Nugroho, Ferial Hendarta, Rumaisah Hidayatillah, Firdaus Hassan, Kristovel Printo Nana
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引用次数: 3

摘要

在过去的几十年里,研究开发类似人类的虚拟助手是一项活跃的研究。目前生成模型虚拟助手是向人工通用智能、具有复杂情感和特征的智能计算机迈进的几步。一种构建生成模型虚拟助手的方法是利用LSTM网络。这种方法在构建英语虚拟助手中一直很流行。我们的目的是测试这种方法是否适用于印尼语。我们使用了许多印尼语电影字幕的数据集。我们使用的LSTM变体是嵌入了word2vec的序列到序列模型。结果表明,该模型能较好地回答问候语等简单问题。虽然这些模型未能回答一些复杂的问题,但结果仍然为未来提供了潜在的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual Assistant Using Lstm Networks In Indonesian
Researches into the development of virtual assistants that are human-like today were an active research that has been developed over the past few decades. Currently the generative model virtual assistant is a few steps towards artificial general intelligence, the smart computer with complex feelings and characteristics. One method for building the generative model virtual assistant is by using the LSTM Networks. This method has been popular for building English virtual assistant. Our aim is to test whether this method could be applied in Indonesian or not. We used the dataset from many movie subtitles in Indonesian languages. The LSTM variant that we used was the sequence-to-sequence model embedded with word2vec. The results show that the model could answer appropriately to the simple questions such as greetings. Although the models were failed to answer some complex questions, the results still give potential works on the future.
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