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Found 4 papers, 2 papers with code
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining
However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives).
On Knowledge distillation from complex networks for response prediction
In order to overcome this, we use standard simple models which do not capture all pairwise interactions, but learn to emulate certain characteristics of a complex teacher network.
Towards Exploiting Background Knowledge for Building Conversation Systems
Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them.
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems
("Can you help me in booking a table at this restaurant?").
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