Papers with CEM

2 papers
CEM: Machine-Human Chatting Handoff via Causal-Enhance Module (2022.emnlp-main)

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Challenge: Existing methods to predict chatbot failure ignore causal variables, resulting in cost increasement and prediction bias.
Approach: They propose a machine-human chatting handoff module that predicts chatbot failure . they use user state and labor cost to correct the prediction bias .
Outcome: The proposed method improves the performance of existing methods without any elaborate model crafting.
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)

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Challenge: Existing methods for data augmentation do not fully exploit the potential of DA in NLP.
Approach: They propose an easy and plug-in framework for data augmentation to support effective text classification.
Outcome: The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks.

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