This paper describes our system used in the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. To address the highly subjective nature of emotion detection tasks, we propose a model ensemble strategy designed to capture the varying subjective perceptions of different users towards textual content. The base models of this ensemble strategy consist of several large language models, which are then combined using methods such as neural networks, decision trees, linear regression, and weighted voting. In Track A, out of 28 languages, our system achieved first place in 19 languages. In Track B, out of 11 languages, our system ranked first in 10 languages. Furthermore, our system attained the highest average performance across all languages in both Track A and Track B.

This paper describes our system used in the SemEval-2022 Task 09: R2VQ - Competence-based Multimodal Question Answering. We propose a knowledge-enhanced model for predicting answer in QA task, this model use BERT as the backbone. We adopted two knowledge-enhanced methods in this model: the knowledge auxiliary text method and the knowledge embedding method. We also design an answer extraction task pipeline, which contains an extraction-based model, an automatic keyword labeling module, and an answer generation module. Our system ranked 3rd in task 9 and achieved an exact match score of 78.21 and a word-level F1 score of 82.62.

The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to identify spans, then to classify the technique in the input sequence. But it severely suffers from handling the overlapping in nested span. Then we propose to formulize the task as a question answering task by MRC framework which achieves a better result compared to the pipeline method. Moreover, data augmentation and loss design techniques are also explored to alleviate the problem of data sparse and imbalance. Finally, we attain the 3rd place in the final evaluation phase.