Fetching the paper…
Reading the bibliography…
For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity.
Comparison of diverse decoding methods from conditional language models
Ippolito, D., Kriz, R., Kustikova, M., Sedoc, J., and Callison-Burch, C · 1906
Earlier work this paper cites.
Human and automatic detection of generated text
Ippolito, D., Duckworth, D., Callison-Burch, C., and Eck, D · 1911
Earlier work this paper cites.
A mathematical theory of communication
Shannon, C. E · 1948
Earlier work this paper cites.
Von neumann’s comparison method for random sampling from the normal and other distributions
Forsythe, G. E · 1972
Earlier work this paper cites.
Time series analysis , volume 2
Hamilton, J. D · 1994
Earlier work this paper cites.
Pharaoh: a beam search decoder for phrase-based statistical machine translation models
Koehn, P · 2004
Earlier work this paper cites.
Discriminative reranking for machine translation
Shen, L., Sarkar, A., and Och, F. J · 2004
Earlier work this paper cites.
Computing machinery and intelligence
Turing, A. M · 2009
Earlier work this paper cites.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Earlier work this paper cites.
Mutual information and diverse decoding improve neural machine translation
Li, J. and Jurafsky, D · 2016
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, W. B · 2016
Cited alongside, same era.
Diverse beam search: Decoding diverse solutions from neural sequence models
Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., and Batra, D · 2016
Cited alongside, same era.
Show and tell: Lessons learned from the 2015 mscoco image captioning challenge
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D · 2016
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N · 2017
Cited alongside, same era.
Yang, Y., Huang, L., and Ma, M · 2018
Later among the works it cites.
Generating informative and diverse conversational responses via adversarial information maximization
Zhang, Y., Galley, M., Gao, J., Gan, Z., Li, X., Brockett, C., and Dolan, B · 2018
Later among the works it cites.
Training language gans from scratch
de Masson d’Autume, C., Mohamed, S., Rosca, M., and Rae, J · 2019
Later among the works it cites.
Unifying human and statistical evaluation for natural language generation
Hashimoto, T. B., Zhang, H., and Liang, P · 2019
Later among the works it cites.
The curious case of neural text degeneration
Holtzman, A., Buys, J., Forbes, M., and Choi, Y · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Koehn, P. and Knowles, R · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
(unconstrained) beam search is sensitive to large search discrepancies
Cohen, E. and Beck, J. C · 2018
Cited alongside, same era.
Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y · 2018
Cited alongside, same era.
Importance of a search strategy in neural dialogue modelling
Kulikov, I., Miller, A. H., Cho, K., and Weston, J · 2018
Cited alongside, same era.
Analyzing uncertainty in neural machine translation
Ott, M., Auli, M., Grangier, D., and Ranzato, M · 2018
Cited alongside, same era.
Diversity-promoting gan: A cross-entropy based generative adversarial network for diversified text generation
Xu, J., Ren, X., Lin, J., and Sun, X · 2018
Cited alongside, same era.
Later among the works it cites.
Complexity-weighted loss and diverse reranking for sentence simplification
Kriz, R., Sedoc, J., Apidianaki, M., Zheng, C., Kumar, G., Miltsakaki, E., and Callison-Burch, C · 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
Later among the works it cites.
On NMT search errors and model errors: Cat got your tongue?
Stahlberg, F. and Byrne, B · 2019
Later among the works it cites.
Cluster-based beam search for pointer-generator chatbot grounded by knowledge
Tam, Y.-C., Ding, J., Niu, C., and Zhou, J · 2019
Later among the works it cites.
Neural text generation with unlikelihood training
Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J · 2019
Later among the works it cites.
Dialogpt: Large-scale generative pre-training for conversational response generation
Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., and Dolan, B · 2019
Later among the works it cites.
Towards a human-like open-domain chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al · 2020
Closest in time.