使用深度学习方法的跨语言文本蕴涵

Wubie Belay, M. Meshesha, Dagnachew Melesew
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引用次数: 0

摘要

自然语言处理是处理自然语言理解和自然语言生成,使计算机能够理解和分析人类语言。跨语言文本蕴涵(CLTE)是假设(P)为源语言,假设(H)为目标语言的非语言推理的应用之一。在资源不足的语言(阿姆哈拉语)和资源丰富的语言(英语)之间进行信息传递具有挑战性。为了解决这一问题,我们利用深度神经网络方法提出了跨语言文本蕴涵模型。我们使用Bi-LSTM来传输顺序信息,使用XLNet来处理单词的位置及其边界,使用MLP来分类和预测输出,使用FastText来表示单词。神经机器翻译用于将英语句子翻译成阿姆哈拉语句子,并与IBM5对齐。我们将Amharic数据集与SNLI数据集相结合,并基于多路分类进行标注。NMT预测的测试准确率为96.01%。我们得到了89.92%的训练准确率和86.89%的测试准确率。这项研究的问题在于它忽略了多重推论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-lingual textual entailment using deep learning approach
Natural Language processing is dealing with natural language understandings and natural language generation which enable computers to understand and analyze human language. Cross-lingual Textual Entailment (CLTE) is one of the applications of NLU if there exists premise (P) as a source language and hypothesis (H) as a target language. CLTE is challenging for transferring information between under resource (Amharic) language and high resource (English) language. To solve this problem, we have proposed Cross-lingual Textual Entailment model using deep neural network approaches. We have used Bi-LSTM to transfer sequential information, XLNet for handling a position of word and its boundary, MLP for classification and prediction outputs, and FastText to word representations. Neural machine translation is utilized for translating English sentences into Amharic sentences with IBM5 alignment. We have combined Amharic dataset with SNLI dataset and annotated based on multi-way classification. The NMT predicts 96.01% of the testing accuracy. We have obtained 89.92% training and 86.89% testing accuracy for the proposed model. The issue with this research is that it ignores multiple inferences.
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