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Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maintain entity coherence and consistency.
Towards a’natural’narratology
Fludernik, M · 2002
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Modeling local coherence: An entity-based approach
Barzilay, R. and Lapata, M · 2008
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Character
Jannidis, F · 2009
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Learning latent personas of film characters
Bamman, D., O’Connor, B., and Smith, N. A · 2013
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Graph-based local coherence modeling
Guinaudeau, C. and Strube, M · 2013
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Character and person
Frow, J · 2014
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The archetypes and the collective unconscious
Jung, C. G · 2014
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Normalized entity graph for computing local coherence
Mesgar, M. and Strube, M · 2014
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End-to-end memory networks
Sukhbaatar, S., Weston, J., Fergus, R., et al · 2015
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Exploiting the bipartite structure of entity grids for document coherence and retrieval
Lioma, C., Tarissan, F., Simonsen, J. G., Petersen, C., and Larsen, B · 2016
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Tracking the world state with recurrent entity networks
Henaff, M., Weston, J., Szlam, A., Bordes, A., and LeCun, Y · 2017
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Dynamic entity representations in neural language models
Ji, Y., Tan, C., Martschat, S., Choi, Y., and Smith, N. A · 2017
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Neural text generation in stories using entity representations as context
Clark, E., Ji, Y., and Smith, N. A · 2018
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Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y · 2018
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The narrativeqa reading comprehension challenge
Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E · 2018
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Higher-order coreference resolution with coarse-to-fine inference
Lee, K., He, L., and Zettlemoyer, L · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J. G., Le, Q., and Salakhutdinov, R · 2019
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Proceedings of the Fifth Conference on Machine Translation , Online, November 2020. Association for Computational Linguistics
Barrault, L., Bojar, O., Bougares, F., Chatterjee, R., Costa-jussà, M. R., Federmann, C., Fishel, M., Fraser, A., Graham, Y., Guzman, P., Haddow, B., Huck, M., Yepes, A. J., Koehn, P., Martins, A., Morishita, M., Monz, C., Nagata, M., Nakazawa, T., and Negri, M. (eds.) · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Content planning for neural story generation with aristotelian rescoring
Goldfarb-Tarrant, S., Chakrabarty, T., Weischedel, R., and Peng, N · 2020
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Compressive transformers for long-range sequence modelling
Rae, J. W., Potapenko, A., Jayakumar, S. M., Lillicrap, T. P., Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., et al · 2020
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Plug and play language models: A simple approach to controlled text generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., and Liu, R · 2019
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Strategies for structuring story generation
Fan, A., Lewis, M., and Dauphin, Y · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
Cited alongside, same era.
Ctrl: A conditional transformer language model for controllable generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., and Socher, R · 2019
Cited alongside, same era.
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Plotmachines: Outline-conditioned generation with dynamic plot state tracking
Rashkin, H., Celikyilmaz, A., Choi, Y., and Gao, J · 2020
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Eliciting knowledge from language models using automatically generated prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
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Controllable story generation with external knowledge using large-scale language models
Xu, P., Patwary, M., Shoeybi, M., Puri, R., Fung, P., Anandkumar, A., and Catanzaro, B · 2020
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All that’s ‘human’is not gold: Evaluating human evaluation of generated text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., and Smith, N. A · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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