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Jadika, K., Khoo, C., Na, J.: Literature review writing: A study of information selection from cited papers. In: Asia Pacific Conference Library & Information Education & Practice, pp. 467–477 (2011)
2011
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Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
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2013
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Hu, Y., Wan, X.: Automatic generation of related work sections in scientific papers: an optimization approach. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1624–1633 (2014)
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Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)
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Wen, T.-H., Gasic, M., Mrksic, N., Su, P.-H., Vandyke, D., Young, S.: Semantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745 (2015)
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Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. Advances in neural information processing systems 28
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Ranzato, M., Chopra, S., Auli, M., Zaremba, W.: Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732 (2015)
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2015
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Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016)
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Ghazvininejad, M., Shi, X., Priyadarshi, J., Knight, K.: Hafez: an interactive poetry generation system. In: Proceedings of ACL 2017, System Demonstrations, pp. 43–48 (2017)
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Li, J., Monroe, W., Jurafsky, D.: Learning to decode for future success. arXiv preprint arXiv:1701.06549 (2017)
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Gu, J., Cho, K., Li, V.O.: Trainable greedy decoding for neural machine translation. arXiv preprint arXiv:1702.02429 (2017)
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Nguyen, K., Daumé III, H., Boyd-Graber, J.: Reinforcement learning for bandit neural machine translation with simulated human feedback. arXiv preprint arXiv:1707.07402 (2017)
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2017
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Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)
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Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences. Advances in neural information processing systems 30
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Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? arXiv preprint arXiv:1801.07243 (2018)
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2018
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Niu, T., Bansal, M.: Polite dialogue generation without parallel data. Transactions of the Association for Computational Linguistics 6
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Zhou, K., Prabhumoye, S., Black, A.W.: A dataset for document grounded conversations. arXiv preprint arXiv:1809.07358 (2018)
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Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241 (2018)
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Ghazvininejad, M., Brockett, C., Chang, M.-W., Dolan, B., Gao, J., Yih, W.-t., Galley, M.: A knowledge-grounded neural conversation model. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
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Liu, Y., Lapata, M.: Learning structured text representations. Transactions of the Association for Computational Linguistics 6
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Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: Exploration and evaluation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
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Romanov, A., Rumshisky, A., Rogers, A., Donahue, D.: Adversarial decomposition of text representation. arXiv preprint arXiv:1808.09042 (2018)
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Rao, S., Tetreault, J.: Dear sir or madam, may i introduce the gyafc dataset: Corpus, benchmarks and metrics for formality style transfer. arXiv preprint arXiv:1803.06535 (2018)
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Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
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Fan, A., Lewis, M., Dauphin, Y.: Hierarchical neural story generation. arXiv preprint arXiv:1805.04833 (2018)
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Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., Choi, Y.: Learning to write with cooperative discriminators. arXiv preprint arXiv:1805.06087 (2018)
Original
2018
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Similar

Logeswaran, L., Lee, H., Bengio, S.: Content preserving text generation with attribute controls. Advances in Neural Information Processing Systems 31
2018
Cited alongside, same era.
Li, W., Xiao, X., Lyu, Y., Wang, Y.: Improving neural abstractive document summarization with explicit information selection modeling. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1787–1796 (2018)
2018
Cited alongside, same era.
Peng, N., Ghazvininejad, M., May, J., Knight, K.: Towards controllable story generation. In: Proceedings of the First Workshop on Storytelling, pp. 43–49 (2018)
2018
Cited alongside, same era.
Wu, Y., Hu, B.: Learning to extract coherent summary via deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Cited alongside, same era.
Tambwekar, P., Dhuliawala, M., Martin, L.J., Mehta, A., Harrison, B., Riedl, M.O.: Controllable neural story plot generation via reinforcement learning. arXiv preprint arXiv:1809.10736 (2018)

Then

Balachandran, V., Pagnoni, A., Lee, J.Y., Rajagopal, D., Carbonell, J., Tsvetkov, Y.: Structsum: Summarization via structured representations. arXiv preprint arXiv:2003.00576 (2020)
Original
2020
Later among the works it cites.
Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)
Original
2020
Later among the works it cites.
Prabhumoye, S., Black, A.W., Salakhutdinov, R.: Exploring controllable text generation techniques. arXiv preprint arXiv:2005.01822 (2020)
Original
2020
Later among the works it cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
2020
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2023

Advancements in Scientific Controllable Text Generation Methods

Goel, Arnav, Hira, Medha, Anand, Avinash et al.

Understand

The previous work on controllable text generation is organized using a new schema we provide in this study.

Seven components make up the schema, and each one is crucial to the creation process.
To accomplish controlled generation for scientific literature, we describe the various modulation strategies utilised to modulate each of the seven components.
We also offer a theoretical study and qualitative examination of these methods.

Built on

Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural computation 1
1989
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9
1997
Earlier work this paper cites.
Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 311–318 (2002)
2002
Earlier work this paper cites.
Hoang, C.D.V., Kan, M.-Y.: Towards automated related work summarization. In: Coling 2010: Posters, pp. 427–435 (2010)
2010
Earlier work this paper cites.
Jadika, K., Khoo, C., Na, J.: Literature review writing: A study of information selection from cited papers. In: Asia Pacific Conference Library & Information Education & Practice, pp. 467–477 (2011)
2011
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
Earlier work this paper cites.
Hu, Y., Wan, X.: Automatic generation of related work sections in scientific papers: an optimization approach. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1624–1633 (2014)
2014
Earlier work this paper cites.
Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)
Original
2015
Earlier work this paper cites.
Wen, T.-H., Gasic, M., Mrksic, N., Su, P.-H., Vandyke, D., Young, S.: Semantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745 (2015)
Original
2015
Earlier work this paper cites.
Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Ranzato, M., Chopra, S., Auli, M., Zaremba, W.: Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732 (2015)
Original
2015
Earlier work this paper cites.
Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016)
Original
2016
Earlier work this paper cites.
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Original
2016
Earlier work this paper cites.
See, A., Liu, P.J., Manning, C.D.: Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368 (2017)
Original
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Ghazvininejad, M., Shi, X., Priyadarshi, J., Knight, K.: Hafez: an interactive poetry generation system. In: Proceedings of ACL 2017, System Demonstrations, pp. 43–48 (2017)
2017
Earlier work this paper cites.
Li, J., Monroe, W., Jurafsky, D.: Learning to decode for future success. arXiv preprint arXiv:1701.06549 (2017)
Original
2017
Earlier work this paper cites.
Gu, J., Cho, K., Li, V.O.: Trainable greedy decoding for neural machine translation. arXiv preprint arXiv:1702.02429 (2017)
Original
2017
Earlier work this paper cites.
Nguyen, K., Daumé III, H., Boyd-Graber, J.: Reinforcement learning for bandit neural machine translation with simulated human feedback. arXiv preprint arXiv:1707.07402 (2017)
Original
2017
Earlier work this paper cites.
Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)
Original
2017
Earlier work this paper cites.
Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? arXiv preprint arXiv:1801.07243 (2018)
Original
2018
Earlier work this paper cites.
Niu, T., Bansal, M.: Polite dialogue generation without parallel data. Transactions of the Association for Computational Linguistics 6
2018
Earlier work this paper cites.
Zhou, K., Prabhumoye, S., Black, A.W.: A dataset for document grounded conversations. arXiv preprint arXiv:1809.07358 (2018)
Original
2018
Earlier work this paper cites.
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241 (2018)
Original
2018
Earlier work this paper cites.
Ghazvininejad, M., Brockett, C., Chang, M.-W., Dolan, B., Gao, J., Yih, W.-t., Galley, M.: A knowledge-grounded neural conversation model. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Earlier work this paper cites.
Liu, Y., Lapata, M.: Learning structured text representations. Transactions of the Association for Computational Linguistics 6
2018
Earlier work this paper cites.
Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: Exploration and evaluation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Earlier work this paper cites.
Romanov, A., Rumshisky, A., Rogers, A., Donahue, D.: Adversarial decomposition of text representation. arXiv preprint arXiv:1808.09042 (2018)
Original
2018
Earlier work this paper cites.
Rao, S., Tetreault, J.: Dear sir or madam, may i introduce the gyafc dataset: Corpus, benchmarks and metrics for formality style transfer. arXiv preprint arXiv:1803.06535 (2018)
Original
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Fan, A., Lewis, M., Dauphin, Y.: Hierarchical neural story generation. arXiv preprint arXiv:1805.04833 (2018)
Original
2018
Earlier work this paper cites.
Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., Choi, Y.: Learning to write with cooperative discriminators. arXiv preprint arXiv:1805.06087 (2018)
Original
2018
Earlier work this paper cites.

Similar

Logeswaran, L., Lee, H., Bengio, S.: Content preserving text generation with attribute controls. Advances in Neural Information Processing Systems 31
2018
Cited alongside, same era.
Li, W., Xiao, X., Lyu, Y., Wang, Y.: Improving neural abstractive document summarization with explicit information selection modeling. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1787–1796 (2018)
2018
Cited alongside, same era.
Peng, N., Ghazvininejad, M., May, J., Knight, K.: Towards controllable story generation. In: Proceedings of the First Workshop on Storytelling, pp. 43–49 (2018)
2018
Cited alongside, same era.
Wu, Y., Hu, B.: Learning to extract coherent summary via deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Cited alongside, same era.
Tambwekar, P., Dhuliawala, M., Martin, L.J., Mehta, A., Harrison, B., Riedl, M.O.: Controllable neural story plot generation via reinforcement learning. arXiv preprint arXiv:1809.10736 (2018)

Then

Balachandran, V., Pagnoni, A., Lee, J.Y., Rajagopal, D., Carbonell, J., Tsvetkov, Y.: Structsum: Summarization via structured representations. arXiv preprint arXiv:2003.00576 (2020)
Original
2020
Later among the works it cites.
Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)
Original
2020
Later among the works it cites.
Prabhumoye, S., Black, A.W., Salakhutdinov, R.: Exploring controllable text generation techniques. arXiv preprint arXiv:2005.01822 (2020)
Original
2020
Later among the works it cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2018

Cited alongside, same era.

Tang, J., Zhao, T., Xiong, C., Liang, X., Xing, E.P., Hu, Z.: Target-guided open-domain conversation. arXiv preprint arXiv:1905.11553 (2019)

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Wang, K., Hua, H., Wan, X.: Controllable unsupervised text attribute transfer via editing entangled latent representation. Advances in Neural Information Processing Systems 32

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Cohan, A., Ammar, W., van Zuylen, M., Cady, F.: Structural scaffolds for citation intent classification in scientific publications. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 3586–3596. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1361 . https://aclanthology.org/N19-1361

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Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)

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Wallace, E., Feng, S., Kandpal, N., Gardner, M., Singh, S.: Universal adversarial triggers for attacking and analyzing nlp. arXiv preprint arXiv:1908.07125 (2019)

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Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32

2019

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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al

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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J., et al

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