Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels
Yue Chen, Tianwei He, Hongbin Zhou, Jia-Chen Gu, Heng Lu, Zhen-Hua Ling · 2023
Speaker identification in novel dialogues can be widely applied to various downstream tasks, such as producing multi-speaker audiobooks and converting novels into scripts.However, existing state-of-the-art methods are limited to handling explicit narrative patterns like "Tom said, '...'", unable to thoroughly understand long-range contexts and to deal with complex cases.To this end, we propose a framework named SPC, which identifies implicit speakers in novels via symbolization, prompt, and classification.First, SPC symbolizes the mentions of candidate speakers to construct a unified label set.Then, by inserting a prompt we reformulate speaker identification as a classification task to minimize the gap between the training objectives of speaker identification and the pre-training task.Two auxiliary tasks are also introduced in SPC to enhance long-range context understanding.Experimental results show that SPC outperforms previous methods by a large margin of 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and also outperforms the emerging ChatGPT.In addition, SPC is more accurate in implicit speaker identification cases that require long-range context semantic understanding.