Mingxing Xu


Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Yingmei Guo | Linjun Shou | Jian Pei | Ming Gong | Mingxing Xu | Zhiyong Wu | Daxin Jiang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.


Guoym at SemEval-2020 Task 8: Ensemble-based Classification of Visuo-Lingual Metaphor in Memes
Yingmei Guo | Jinfa Huang | Yanlong Dong | Mingxing Xu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we describe our ensemble-based system designed by guoym Team for the SemEval-2020 Task 8, Memotion Analysis. In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects. We train five base classifiers for each type of representation using five-fold cross-validation. Then the outputs of these base classifiers are combined through data-based ensemble method and feature-based ensemble method to make full use of all data and representations from different aspects. Our method achieves the performance within the top 2 ranks in the final leaderboard of Memotion Analysis among 36 Teams.

FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues
Yingmei Guo | Zhiyong Wu | Mingxing Xu
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.


THU-HCSI at SemEval-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in Conversation
Xihao Liang | Ye Ma | Mingxing Xu
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we describe our hierarchical ensemble system designed for the SemEval-2019 task3, EmoContext. In our system, three sets of classifiers are trained for different sub-targets and the predicted labels of these base classifiers are combined through three steps of voting to make the final prediction. Effective details for developing base classifiers are highlighted.


Lyric-based Song Sentiment Classification with Sentiment Vector Space Model
Yunqing Xia | Linlin Wang | Kam-Fai Wong | Mingxing Xu
Proceedings of ACL-08: HLT, Short Papers


Rejection in Speech Recognition Based on CDCPMs
Mingxing Xu | Fang Zheng | Wenhu Wu
Proceedings of the 10th Research on Computational Linguistics International Conference

The Description of the Intra-State Feature Space in Speech Recognition
Fang Zheng | Mingxing Xu | Wenhu Wu
ROCLING 1997 Poster Papers