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Understanding and Generating Dialogue between Characters in Stories: Limitations and Referencesby@teleplay
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Understanding and Generating Dialogue between Characters in Stories: Limitations and References

by Teleplay Technology May 9th, 2024
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Exploring machine understanding of story dialogue via new tasks and dataset, improving coherence and speaker recognition in storytelling AI.
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Authors:

(1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology.

Abstract and Intro

Related Works

DIALSTORY Dataset

Proposed Tasks

Methodology

Experiments

Discussion

Future Work

Conclusion

Limitations and References

10 Limitations

For the DialSpk task, in the test set, there are 150 stories, and a total of 728 masked dialogue positions, and in the validation, there are 100 stories and 505 positions. For the DAC score, there is enough data to evaluate the models’ performance. The dataset is small to some degree for the SAC evaluation. Although the validity of our model could be reflected in this dataset, we also plan to augment this annotated dataset for future research.

References

Mahmoud Azab, Noriyuki Kojima, Jia Deng, and Rada Mihalcea. 2019. Representing movie characters in dialogues. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 99–109, Hong Kong, China. Association for Computational Linguistics.


Faeze Brahman and Snigdha Chaturvedi. 2020. Modeling protagonist emotions for emotion-aware storytelling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5277–5294, Online. Association for Computational Linguistics.


Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, and Snigdha Chaturvedi. 2021. “let your characters tell their story”: A dataset for character-centric narrative understanding. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1734–1752, Punta Cana, Dominican Republic. Association for Computational Linguistics.


Elizabeth Clark, Yangfeng Ji, and Noah A Smith. 2018. Neural text generation in stories using entity representations as context. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2250–2260.


Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting pretrained models for chinese natural language processing. arXiv preprint arXiv:2004.13922.


Li Deng, Gokhan Tur, Xiaodong He, and Dilek Hakkani-Tur. 2012. Use of kernel deep convex networks and end-to-end learning for spoken language understanding. In 2012 IEEE Spoken Language Technology Workshop (SLT), pages 210–215. IEEE.


Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.


Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. 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), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.


Stefan Elfwing, Eiji Uchibe, and Kenji Doya. 2018. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107:3–11.


Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898.


Joseph L Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378.


Jian Guan, Zhuoer Feng, Yamei Chen, Ruilin He, Xiaoxi Mao, Changjie Fan, and Minlie Huang. 2022. Lot: A story-centric benchmark for evaluating chinese long text understanding and generation. Transactions of the Association for Computational Linguistics, 10:434–451.


Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020. A knowledge-enhanced pretraining model for commonsense story generation. Transactions of the Association for Computational Linguistics, 8:93–108.


Jian Guan and Minlie Huang. 2020. UNION: an unreferenced metric for evaluating open-ended story generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 9157–9166. Association for Computational Linguistics.


Jian Guan, Yansen Wang, and Minlie Huang. 2019. Story ending generation with incremental encoding and commonsense knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6473–6480.


Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, and Noah A Smith. 2017. Dynamic entity representations in neural language models. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1830–1839.


XJ Kennedy, Dana Gioia, and Dan Stone. 1983. Literature: An introduction to fiction, poetry, drama, and writing.


Xiangzhe Kong, Jialiang Huang, Ziquan Tung, Jian Guan, and Minlie Huang. 2021. Stylized story generation with style-guided planning. In Findings of the Association for Computational Linguistics: ACLIJCNLP 2021, pages 2430–2436, Online. Association for Computational Linguistics.


Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055.


Wanshui Li, Yifan Bai, Jiaxuan Lu, and Kexin Yi. 2022. Immersive text game and personality classification. arXiv preprint arXiv:2203.10621.


Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.


Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.


Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outlineconditioned generation with dynamic plot state tracking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4274–4295.


Iulian Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31.


Lifeng Shang, Zhengdong Lu, and Hang Li. 2015. Neural responding machine for short-text conversation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1577–1586.


Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, and Xipeng Qiu. 2021. Cpt: A pre-trained unbalanced transformer for both chinese language understanding and generation. arXiv preprint arXiv:2109.05729.


Tianming Wang and Xiaojun Wan. 2019. T-CVAE: transformer-based conditioned variational autoencoder for story completion. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5233–5239. ijcai.org.


Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, et al. 2021. Kuileixi: a chinese openended text adventure game. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 175–184.


Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, and Bryan Catanzaro. 2020. MEGATRON-CNTRL: controllable story generation with external knowledge using large-scale language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 2831–2845. Association for Computational Linguistics.


Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019. Planand-write: Towards better automatic storytelling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 7378–7385.


Zhexin Zhang, Jiaxin Wen, Jian Guan, and Minlie Huang. 2022. Persona-guided planning for controlling the protagonist’s persona in story generation. arXiv preprint arXiv:2204.10703.


Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, and Xiaoyong Du. 2019. Uer: An open-source toolkit for pretraining models. EMNLP-IJCNLP 2019, page 241.


Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2019. Story ending selection by finding hints from pairwise candidate endings. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(4):719–729.


This paper is available on arxiv under CC 4.0 DEED license.