随着深度学习的迅速发展和领域数据的快速积累,领域化的预训练模型在知识组织和挖掘中发挥了越来越重要的支撑作用。面向海量的中文政策文本,结合相应的预训练策略构建中文政策文本预训练模型,不仅有助于提升中文政策文本智能化处理的水...随着深度学习的迅速发展和领域数据的快速积累,领域化的预训练模型在知识组织和挖掘中发挥了越来越重要的支撑作用。面向海量的中文政策文本,结合相应的预训练策略构建中文政策文本预训练模型,不仅有助于提升中文政策文本智能化处理的水平,而且为政策文本数据驱动下的精细化和多维度分析与探究奠定了坚实的基础。面向国家级、省级和市级平台上的政策文本,通过自动抓取和人工辅助相结合的方式,在去除非政策文本的基础上,确定了131390份政策文本,总字数为305648206。面向所构建的中文政策文本语料库,基于BERT-base-Chinese和Chinese-RoBERTa-wwm-ext,本研究利用MLM(masked language model)和WWM(whole word masking)任务构建了中文政策文本预训练模型(ChpoBERT),并在Github上对该模型进行了开源。在困惑度评价指标和政策文本自动分词、词性自动标注、命名实体识别下游任务上,ChpoBERT系列模型均表现出了较优的性能,可为政策文本的智能知识挖掘提供领域化的基础计算资源支撑。展开更多
In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art perfo...In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.展开更多
文摘随着深度学习的迅速发展和领域数据的快速积累,领域化的预训练模型在知识组织和挖掘中发挥了越来越重要的支撑作用。面向海量的中文政策文本,结合相应的预训练策略构建中文政策文本预训练模型,不仅有助于提升中文政策文本智能化处理的水平,而且为政策文本数据驱动下的精细化和多维度分析与探究奠定了坚实的基础。面向国家级、省级和市级平台上的政策文本,通过自动抓取和人工辅助相结合的方式,在去除非政策文本的基础上,确定了131390份政策文本,总字数为305648206。面向所构建的中文政策文本语料库,基于BERT-base-Chinese和Chinese-RoBERTa-wwm-ext,本研究利用MLM(masked language model)和WWM(whole word masking)任务构建了中文政策文本预训练模型(ChpoBERT),并在Github上对该模型进行了开源。在困惑度评价指标和政策文本自动分词、词性自动标注、命名实体识别下游任务上,ChpoBERT系列模型均表现出了较优的性能,可为政策文本的智能知识挖掘提供领域化的基础计算资源支撑。
基金This work was supported by the Institute for Big Data Analytics and Artificial Intelligence(IBDAAI),Universiti Teknologi Mara,Shah Alam,Selangor.Malaysia.
文摘In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.
