Ellipsis Resolution: Generative Adversarial Networks and Fine-Tuning with Large-Scale Models for Improved Performance

Ellipsis phenomenon is prevalent, particularly in languages like Chinese, and can be observed across various domains, including everyday conversations, literary works, and product evaluations. Such ellipsis poses challenges for machines, affecting the performance of natural language processing tasks such as machine translation and comprehension. Current approaches to tackle ellipsis primarily involve fine-tuning pretrained language models like BERT(Bidirectional Encoder Representations from Transformers). This approach encompasses two subtasks: 1) detecting ellipsis positions, i.e., identifying locations where ellipsis occurs within a sentence, and 2) completing the elliptical content by predicting the missing information based on the detected positions. This paper proposes a novel model that combines fine-tuned BERT with Generative Adversarial Networks (GANs), incorporating a sampler between the generator and discriminator. The proposed method simultaneously utilizes the generator, discriminator, and sampler to locate and complete ellipsis. Experimental results demonstrate that our approach achieves an F1 score improvement of 0.03 in ellipsis position detection and an EM (Exact Match) improvement of 0.1 in ellipsis content completion compared to the baseline method.