@inproceedings{lee-chang-2023-story,
title = "Story Co-telling Dialogue Generation based on Multi-Agent Reinforcement Learning and Story Highlights",
author = "Lee, Yu-Kai and
Chang, Chia-Hui",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2023",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.alta-1.2",
pages = "11--19",
abstract = "Retelling a story is one way to develop narrative skills in students, but it may present some challenges for English as Second Language (ESL) students who are learning new stories and vocabularies at the same time. The goal of this research is to develop a dialogue module for story co-telling for ESL students in order to help students to co-narrate an English story and enhance their narrative skills. However, story co-telling is a relatively underexplored and novel task. In order to understand the story content and select the right plot to continue the story co-telling based on the current dialogue, we utilize open domain information extraction techniques to construct a knowledge graph, and adopt multi-agent reinforcement learning methods to train two agents to select relevant facts from the knowledge graph and generate responses, jointly accomplishing the task of story co-telling. Compared to models that reply on chronological order, our model improves the performance from 67.0{\%} to 70.8{\%} through self-training with reward evaluation, achieving an increase of approximately 3.8{\%}.",
}
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<abstract>Retelling a story is one way to develop narrative skills in students, but it may present some challenges for English as Second Language (ESL) students who are learning new stories and vocabularies at the same time. The goal of this research is to develop a dialogue module for story co-telling for ESL students in order to help students to co-narrate an English story and enhance their narrative skills. However, story co-telling is a relatively underexplored and novel task. In order to understand the story content and select the right plot to continue the story co-telling based on the current dialogue, we utilize open domain information extraction techniques to construct a knowledge graph, and adopt multi-agent reinforcement learning methods to train two agents to select relevant facts from the knowledge graph and generate responses, jointly accomplishing the task of story co-telling. Compared to models that reply on chronological order, our model improves the performance from 67.0% to 70.8% through self-training with reward evaluation, achieving an increase of approximately 3.8%.</abstract>
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%0 Conference Proceedings
%T Story Co-telling Dialogue Generation based on Multi-Agent Reinforcement Learning and Story Highlights
%A Lee, Yu-Kai
%A Chang, Chia-Hui
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F lee-chang-2023-story
%X Retelling a story is one way to develop narrative skills in students, but it may present some challenges for English as Second Language (ESL) students who are learning new stories and vocabularies at the same time. The goal of this research is to develop a dialogue module for story co-telling for ESL students in order to help students to co-narrate an English story and enhance their narrative skills. However, story co-telling is a relatively underexplored and novel task. In order to understand the story content and select the right plot to continue the story co-telling based on the current dialogue, we utilize open domain information extraction techniques to construct a knowledge graph, and adopt multi-agent reinforcement learning methods to train two agents to select relevant facts from the knowledge graph and generate responses, jointly accomplishing the task of story co-telling. Compared to models that reply on chronological order, our model improves the performance from 67.0% to 70.8% through self-training with reward evaluation, achieving an increase of approximately 3.8%.
%U https://aclanthology.org/2023.alta-1.2
%P 11-19
Markdown (Informal)
[Story Co-telling Dialogue Generation based on Multi-Agent Reinforcement Learning and Story Highlights](https://aclanthology.org/2023.alta-1.2) (Lee & Chang, ALTA 2023)
ACL