Papers by Yingmei Guo

2 papers
FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues (2020.aacl-main)

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Challenge: Existing methods for emotion recognition in dialogues do not consider the content of the target utterance.
Approach: They propose to model historical utterances without considering the content of the target utterant . they propose to use a fine-grained reasoning network to generate target-specific historical .
Outcome: The proposed method achieves competitive performance compared with previous methods.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (2021.emnlp-main)

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Challenge: Existing approaches to scale out spoken language understanding to low-resource languages are noisy.
Approach: They propose a method for mitigating noise in augmented data by training models with augmented datasets.
Outcome: The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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