A Unified Multi-Task Learning Model for C hinese Essay Rhetoric Recognition and Component Extraction (2024)

Abstract: “In this paper, we present our system at CCL24-Eval Task 6: Chinese Essay Rhetoric Recognition and Understanding (CERRU). The CERRU task aims to identify and understand the use of rhetoric in student writing. The evaluation set three tracks to examine the recognition of rhetorical form, rhetorical content, and the extract of rhetorical components. Considering the potential correlation among the track tasks, we employ the unified multi-task learning architecture to fully incorporate the inherent interactions among the related tasks to improve the overall performance and to complete the above 3 track tasks with a single model. Specifically, the framework mainly consists of four sub-tasks: rhetorical device recognition, rhetorical form recognition, rhetorical content recognition, and rhetorical component extraction. The first three tasks are regarded as multi-label classification tasks, and the last task is regarded as an entity recognition task. The four tasks leverage potential information transfer to achieve fusion learning. Finally, the above four sub-tasks are integrated into a unified model through parameter sharing. In the final evaluation results, our system ranked fourth with a total score of 60.14, verifying the effectiveness of our approach.”

## introduction in the learning process of primary and secondary school students, rhetorical devices are not only a core component of reading comprehension and writing skills but also an indispensable element in shaping excellent literary works. identifying and understanding the use of rhetoric in students' compositions can significantly enhance their expressive skills and guide them in producing higher-quality narratives and descriptions. the cerru task systematically defines the fine-grained rhetorical types found in primary and secondary school compositions, as detailed in given the small number of training samples and the potential correlation between the track tasks, we have adopted a unified multi-task learning architecture to fully incorporate the inherent interactions among these related tasks, aiming to enhance learning efficiency and prediction accuracy @xcite . by combining all tasks into a single model, we reduce computation and enable simultaneous completion of the three track tasks @xcite . specifically, our framework consists of four additionally, we experimented with five different mainstream transformer-based pre-trained language models as backbone networks to assess their performance on the task. given that multi-task learning requires optimizing models for multiple objectives, we also experimented with four different loss weighting schemes to approach the optimal performance of the model. in this paper, our contributions can be summarized in three main aspects: ## methodology to fully leverage the potential correlation between each task, we employ a multi-task learning framework. this approach can be seen as an inductive knowledge transfer method that improves generalization by sharing domain information across complementary tasks. by learning multiple tasks using shared representations, insights gained from one task can aid in learning the others @xcite . additionally, to further enhance the model's generalization ability, we incorporate adversarial training methods during model training. ## conclusion in this paper, we propose a unified multi-task learning framework for ccl24-eval task 6 (cerru), to enhance feature fusion and interaction among subtasks, and achieve a single model capable of completing all subtasks. we experimented with various pre-trained language models and weighting methods, and further improved our experimental results through model voting. our experiments demonstrate that our proposed approach achieves good results in this evaluation. however, there are still several shortcomings in this system. in the future, we plan to enhance the model's performance through data augmentation and domain-specific pre-training. additionally, we intend to explore more suitable weighting methods for this evaluation.