Negative Training for Neural Dialogue Response Generation
Tianxing He, James Glass
Dialogue and Interactive Systems Long Paper
Session 4A: Jul 6
(17:00-18:00 GMT)
Session 5A: Jul 6
(20:00-21:00 GMT)
Abstract:
Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named ``Negative Training" to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses, or discourage frequent responses and improve response diversity.
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