传统的视频字幕生成模型大多都采用编码器—译码器框架。在编码阶段,使用卷积神经网络对视频进行处理。在解码阶段,使用长短期记忆网络生成视频的相应字幕。基于视频的时序相关性和多模态性,提出了一个混合型模型,即基于硬注意力的多模态视频字幕的生成模型。该模型在编码阶段使用不同的融合模型将视频和音频两种模态进行关联,在解码阶段基于长短期记忆网络的基础上加入了硬注意力机制来生成对视频的描述。这个混合模型在数据集MSR-VTT(Microsoft research video to text)上得到的机器翻译指标较基础模型有0.2%~3.8%的提升。根据实验结果可以判定基于硬注意力机制的多模态混合模型可以生成视频的精准描述字幕。 相似文献
针对提高飞机辅助动力装置(auxiliary power unit,APU)排气温度(exhaust gas temperature,EGT)参数的预测精度问题,提出了一种基于特征选择和多尺度卷积-长短期记忆网络编码器-解码器的EGT预测模型.首先,利用随机森林方法确定重要度较高的APU监测参数;其次,利用多尺度卷积神... 相似文献
从无结构化自然语言文本中抽取实体关系三元组是构建大型知识图谱中最为关键的一步,但现有研究仍存在3方面问题:1)忽略文本中因多个三元组共享同一实体而产生的实体关系重叠问题;2)当前以编码器−解码器为基础的联合抽取模型未充分考虑文本语句词之间的依赖关系;3)部分三元组序列过长导致误差累积与传播,影响实体关系抽取的精度和效率.基于此,提出基于图卷积增强多路解码的实体关系联合抽取模型 (graph convolution-enhanced multi-channel decoding joint entity and relation extraction model, GMCD-JERE).首先,基于BiLSTM作为模型编码器,强化文本中词的双向特征融合;其次,通过图卷积多跳特征融合句中词之间的依赖关系,提高关系抽取准确性;此外,改进传统模型按三元组先后顺序的解码机制,通过多路解码三元组机制,解决实体关系重叠问题,同时缓解三元组序列过长造成误差累积、传播的影响;最后,实验选用当前3个主流模型进行性能验证,在NYT (New York times)数据集上结果表明在精确率、召回率和F1这3个指标上分别提升了4.3%,5.1%,4.8%,同时在WebNLG (Web natural language generation)数据集上验证以关系为开始的抽取顺序.
Keyphrase greatly provides summarized and valuable information. This information can help us not only understand text semantics, but also organize and retrieve text content effectively. The task of automatically generating it has received considerable attention in recent decades. From the previous studies, we can see many workable solutions for obtaining keyphrases. One method is to divide the content to be summarized into multiple blocks of text, then we rank and select the most important content. The disadvantage of this method is that it cannot identify keyphrase that does not include in the text, let alone get the real semantic meaning hidden in the text. Another approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text, but the inherently sequential nature precludes parallelization within training examples, and distances have limitations on context dependencies. Previous works have demonstrated the benefits of the self-attention mechanism, which can learn global text dependency features and can be parallelized. Inspired by the above observation, we propose a keyphrase generation model, which is based entirely on the self-attention mechanism. It is an encoder-decoder model that can make up the above disadvantage effectively. In addition, we also consider the semantic similarity between keyphrases, and add semantic similarity processing module into the model. This proposed model, which is demonstrated by empirical analysis on five datasets, can achieve competitive performance compared to baseline methods. 相似文献