Understanding and Generating Dialogue between Characters in Stories: Related Works
by Teleplay Technology May 9th, 2024
Too Long; Didn't Read
Exploring machine understanding of story dialogue via new tasks and dataset, improving coherence and speaker recognition in storytelling AI.
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.
Table of Links
2 Related Works
Story Understanding and Generation Recently there have been various tasks proposed to assess the ability of story understanding and generation such as story ending selection (Zhou et al., 2019), story ending generation (Guan et al., 2019), story completion (Wang and Wan, 2019), story character identification (Brahman et al., 2021), story generation from prompts (Fan et al., 2018), titles (Yao et al., 2019) and beginnings (Guan et al., 2020). Another line of work focused on controllable attributes in story generation such as keywords (Xu et al., 2020), outlines (Rashkin et al., 2020), emotional trajectories (Brahman and Chaturvedi, 2020), styles (Kong et al., 2021) and characters’ personalities (Zhang et al., 2022). Different from them, we emphasize the importance of dialogue in developing characters and maintaining the story’s coherence.
Dialogue in story The dialogue context is comprised of a variety of messages from users and the system, determining the conversation topic and the user’s goal for the conversation(Serban et al., 2017). The dialogue in the story makes this feature more apparent. Recently, like AI Dungeon, some text adventure games focus on players and machines co-creating stories. Li et al. (2022) proposed the idea that players can play the role of the chosen character and chat with others through the dialogue, while Xi et al. (2021) presented an AI game where players can explore different ways to reach the plot goals. In contrast, our tasks focus on both the understanding and generation of dialogue in the story, and they can be easily generalized to more complex AI interactive games.
Character Representations Dialogue unfolds in a story around several inter-related characters. Accordingly, it is essential to capture the traits of different characters for dialogue modeling. Ji et al. (2017); Clark et al. (2018) proposed to update characters’ representations dynamically based on previous hidden outputs to track their states, which does not apply to the parallel architecture of Transformer for training. Azab et al. (2019) derived character representations from their corresponding dialogue turns. In contrast, we learn character representations from story plots, which then serve to understand and generate dialogue.
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