Next Word Prediction Using Deep Learning

Aditya Tiwari, N. Sengar, Vrinda Yadav
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引用次数: 4

Abstract

Next Word Prediction involves guessing the next word which is most likely to come after the current word. The system suggests a few words. A user can choose a word according to their choice from a list of suggested word by system. It increases typing speed and reduces keystrokes of the user. It is also useful for disabled people to enter text slowly and for those who are not good with spellings. Previous studies focused on prediction of the next word in different languages. Some of them are Bangla, Assamese, Ukraine, Kurdish, English, and Hindi. According to Census 2011, 43.63% of the Indian population uses Hindi, the national language of India. In this work, deep learning techniques are proposed to predict the next word in Hindi language. The paper uses Long Short Term Memory and Bidirectional Long Short Term Memory as the base neural network architecture. The model proposed in this work outperformed the existing approaches and achieved the best accuracy among neural network based approaches on IITB English-Hindi parallel corpus.
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使用深度学习的下一个单词预测
下一个单词预测包括猜测最有可能出现在当前单词之后的下一个单词。系统会提示一些单词。用户可以根据自己的选择从系统推荐的单词列表中选择一个单词。它提高了打字速度,减少了用户的击键次数。对于那些输入文字缓慢的残疾人和拼写不佳的人来说,这也很有用。以前的研究主要集中在预测不同语言中的下一个单词。其中一些是孟加拉语、阿萨姆语、乌克兰语、库尔德语、英语和印地语。根据2011年人口普查,43.63%的印度人口使用印度的国家语言印地语。在这项工作中,提出了深度学习技术来预测印地语中的下一个单词。本文采用长短期记忆和双向长短期记忆作为神经网络的基础架构。本文提出的模型在IITB英语-印地语平行语料库上的准确率优于现有的神经网络方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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