Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

no code implementations • EMNLP 2021 • YingMei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang

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.

Data Augmentation Denoising +1

FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues

no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • YingMei Guo, Zhiyong Wu, Mingxing Xu

Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information.

Emotion Recognition

Guoym at SemEval-2020 Task 8: Ensemble-based Classification of Visuo-Lingual Metaphor in Memes

no code implementations • SEMEVAL 2020 • YingMei Guo, Jinfa Huang, Yanlong Dong, Mingxing Xu

In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects.