也就是通过这种方式,系统可以更加有效地了解什么是好的输出,什么是坏的输出。本文在机器翻译和语义解析这两个较弱的监督任务上,采用了多个坡道损失函数(ramp loss)在神经模型中。 研究方法: Ramp loss的目标函数如下: 公式当中,M是mini batch的大小,y-是坏的输出(fear),需要被阻止,y+是好的输出(hope),需要被...
A neural chatbot using sequence to sequence model with attentional decoder. This is a fully functional chatbot. This is based on Google Translate Tensorflow modelhttps://github.com/tensorflow/models/blob/master/tutorials/rnn/translate/ Sequence to sequence model by Cho et al.(2014) Created by Chi...
GPT2:CarryMeRookie:大模型系列论文 GPT2: Language Models are Unsupervised Multitask Learners Sequence to Sequence Learning with Neural Networks 摘要 深度神经网络(DNNs)是强大的模型,已在困难的学习任务上取得了出色的表现。尽管当有大量标记的训练集可用时,DNNs表现良好,但它们不能用于将序列映射到序列。在本文...
This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyon...
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end app...
Order matters: sequence to sequence for sets. In: Bengio, Y. & LeCun, Y. (eds.) Proc. 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4, 2016, Conference Track Proceedings (2016). Yang, K. et al. Analyzing learned molecular ...
本文是对Sequence to Sequence Learning with Neural Networks文章阅读后的总结。 在不同的学习任务中,传统深度神经网络(DNN)是表现不错的强力模型,如在图像分类、语音识别领域,但DNN由于不能适应输入输出不固定的情况,导致其不能够用于序列到序列的映射任务。 在2014年,Google的三位作者提出基于端到端的序列到序列模...
Abstractive Text Summarization with Sequence-to-Sequence Models Tian Shi,Yaser Keneshloo,Naren Ramakrishnan,Chandan K. Reddy Requirements and Installation Python 3.5.2 glob argparse shutil pytorch 1.0 Use following scripts to Set up GPU, cuda and pytorch ...
2015. Sequence-to- sequence neural net models for grapheme-to-phoneme conversion. Proc. INTERSPEECH, pages 3330-3334.Kaisheng Yao and Geoffrey Zweig. 2015. Sequence- to-sequence neural net models for grapheme-to- phoneme conversion. In INTERSPEECH 2015, pages 3330-3334, Dresden, Germany....
Sequence to Sequence Learning with Neural Networks:使用神经网络来做序列到序列的学习 Abstract 本文提出了一种通用的端 对端的方法进行序列到序 列的学习,其中的 Encoder和Deocder都是 多层的LSTM。我们的模 型在机器翻译上取得了非 常好的效果。 The Model Introduction 为了处理变长的输入和变长的 输出,我们...