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Found 4 papers, 1 papers with code
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data
Therefore, unlike previous studies, we propose a domain-adaptation framework of MRC under the assumption that the only available data in the target domain are human conversations between a user asking questions and an expert answering the questions.
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization
Due to the lack of any annotated real conversational dataset, EEND is usually pretrained on a large-scale simulated conversational dataset first and then adapted to the target real dataset.
Addressing Ambiguity of Emotion Labels Through Meta-Learning
We therefore propose a dynamic label correction and sample contribution weight estimation model.
Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach
Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets.
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