while (1): study();

링크: https://arxiv.org/abs/2005.00661 On Faithfulness and Factuality in Abstractive Summarization It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyze arxiv.org Main Question 1. abstractive summa..

링크: https://arxiv.org/abs/1711.09724 Table-to-text Generation by Structure-aware Seq2seq Learning Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists arxiv.org Table-to-Text Generation에 있어 중요한 ..

링크: https://arxiv.org/abs/2010.00910 Continual Learning for Natural Language Generation in Task-oriented Dialog Systems Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applicat arxiv.org 범용 인공지능에 다가..

링크:https://arxiv.org/abs/1704.04368 Get To The Point: Summarization with Pointer-Generator Networks Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: th arxiv.org 1. Introduction 문서 요약 태스크에는 Ext..

출처: https://arxiv.org/abs/1512.05287 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the arxiv.org LSTM-LM의 성능을 개선하기 ..

출처: https://arxiv.org/abs/1502.03167 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful param arxiv.org 지..

출처: https://arxiv.org/abs/1608.05859 Using the Output Embedding to Improve Language Models We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting upd arxiv.org * 하단 포스팅의 후속 포스팅입니다. https://jcy1996.tis..

출처: https://aclanthology.org/W17-4739/ The University of Edinburgh’s Neural MT Systems for WMT17 Rico Sennrich, Alexandra Birch, Anna Currey, Ulrich Germann, Barry Haddow, Kenneth Heafield, Antonio Valerio Miceli Barone, Philip Williams. Proceedings of the Second Conference on Machine Translation. 2017. aclanthology.org BT(Back Translation)과 BPE(Byte Pair Encoding)로 유명한 Rico Sennrich 교수팀이 WMT17에..

출처: https://dblp.org/rec/conf/aaai/WangXZBQLL18.html dblp: Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). load content from web.archive.org Privacy notice: By enabling the option above, your browser will contact the API of web.arch..

출처: https://arxiv.org/abs/1611.00179 Dual Learning for Machine Translation While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a du arxiv.org 이번에 기계 번역기 구현 다 마치고, colab 환경에서 dual learning을 이용한 fine-..