Fetching the paper…
Reading the bibliography…
Long text generation is an important but challenging task.The main problem lies in learning sentence-level semantic dependencies which traditional generative models often suffer from.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; and Brew, J. 2019 · 1910
Earlier work this paper cites.
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
Li, J.; Zhao, W. X.; Wen, J.; and Song, Y. 2019 · 1979
Earlier work this paper cites.
An information theoretical investigation into the distribution of phonetic information across the auditory spectrogram
Morris, A.; Schwartz, J.-L.; and Escudier, P. 1993 · 1993
Earlier work this paper cites.
Forest-based statistical sentence generation
Langkilde, I. 2000 · 2000
Earlier work this paper cites.
Tensor Graph Convolutional Networks for Text Classification
Liu, X.; You, X.; Zhang, X.; Wu, J.; and Lv, P. 2020 · 2001
Earlier work this paper cites.
Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W. 2002 · 2002
Earlier work this paper cites.
ConceptNet—a practical commonsense reasoning tool-kit
Liu, H.; and Singh, P. 2004 · 2004
Earlier work this paper cites.
Collective content selection for concept-to-text generation
Barzilay, R.; and Lapata, M. 2005 · 2005
Earlier work this paper cites.
Imagination in children’s writing: How high can fiction fly
Colello, S. M. G. 2007 · 2007
Earlier work this paper cites.
Learning to tell tales: A data-driven approach to story generation
McIntyre, N.; and Lapata, M. 2009 · 2009
Earlier work this paper cites.
Using of Jaccard coefficient for keywords similarity
Niwattanakul, S.; Singthongchai, J.; Naenudorn, E.; and Wanapu, S. 2013 · 2013
Earlier work this paper cites.
Sequence to Sequence Learning with Neural Networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
Earlier work this paper cites.
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Earlier work this paper cites.
End-To-End Memory Networks
Sukhbaatar, S.; Szlam, A.; Weston, J.; and Fergus, R. 2015 · 2015
Earlier work this paper cites.
Visual storytelling
Huang, T.-H. K.; Ferraro, F.; Mostafazadeh, N.; Misra, I.; Agrawal, A.; Devlin, J.; Girshick, R.; He, X.; Kohli, P.; Batra, D.; et al. 2016 · 2016
Cited alongside, same era.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Kumar, A.; Irsoy, O.; Ondruska, P.; Iyyer, M.; Bradbury, J.; Gulrajani, I.; Zhong, V.; Paulus, R.; and Socher, R. 2016 · 2016
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Challenges in Data-to-Document Generation
Wiseman, S.; Shieber, S.; and Rush, A. 2017 · 2017
Cited alongside, same era.
Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores
Zang, H.; and Wan, X. 2017 · 2017
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019 · 2019
Later among the works it cites.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ding, M.; Zhou, C.; Chen, Q.; Yang, H.; and Tang, J. 2019 · 2019
Later among the works it cites.
Unified Language Model Pre-training for Natural Language Understanding and Generation
Dong, L.; Yang, N.; Wang, W.; Wei, F.; Liu, X.; Wang, Y.; Gao, J.; Zhou, M.; and Hon, H. 2019 · 2019
Later among the works it cites.
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Guan, J.; Wang, Y.; and Huang, M. 2019 · 2019
Later among the works it cites.
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning
Jiang, Y.; and Bansal, M. 2019 · 2019
Later among the works it cites.
Dynamically Fused Graph Network for Multi-hop Reasoning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Hierarchical Neural Story Generation
Fan, A.; Lewis, M.; and Dauphin, Y. N. 2018 · 2018
Cited alongside, same era.
Topic-to-Essay Generation with Neural Networks
Feng, X.; Liu, M.; Liu, J.; Qin, B.; Sun, Y.; and Liu, T. 2018 · 2018
Cited alongside, same era.
Event Representations for Automated Story Generation with Deep Neural Nets
Martin, L. J.; Ammanabrolu, P.; Wang, X.; Hancock, W.; Singh, S.; Harrison, B.; and Riedl, M. O. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Cited alongside, same era.
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Xu, J.; Ren, X.; Zhang, Y.; Zeng, Q.; Cai, X.; and Sun, X. 2018 · 2018
Cited alongside, same era.
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W. W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
Cited alongside, same era.
Qiu, L.; Xiao, Y.; Qu, Y.; Zhou, H.; Li, L.; Zhang, W.; and Yu, Y. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Later among the works it cites.
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Shao, Z.; Huang, M.; Wen, J.; Xu, W.; and Zhu, X. 2019 · 2019
Later among the works it cites.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Song, K.; Tan, X.; Qin, T.; Lu, J.; and Liu, T. 2019 · 2019
Later among the works it cites.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Tu, M.; Wang, G.; Huang, J.; Tang, Y.; He, X.; and Zhou, B. 2019 · 2019
Later among the works it cites.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
Yang, P.; Luo, F.; Chen, P.; Li, L.; Yin, Z.; He, X.; and Sun, X. 2019 · 2019
Later among the works it cites.
Graph Convolutional Networks for Text Classification
Yao, L.; Mao, C.; and Luo, Y. 2019 · 2019
Later among the works it cites.
Plan-and-Write: Towards Better Automatic Storytelling
Yao, L.; Peng, N.; Weischedel, R. M.; Knight, K.; Zhao, D.; and Yan, R. 2019 · 2019
Later among the works it cites.
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
Guan, J.; Huang, F.; Huang, M.; Zhao, Z.; and Zhu, X. 2020 · 2020
Closest in time.
An Interpretable Reasoning Network for Multi-Relation Question Answering
Zhou, M.; Huang, M.; and Zhu, X. 2018 · 2022
Closest in time.