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Retrieval-based conversation systems generally tend to highly rank responsesthat are semantically similar or even identical to the given conversationcontext. While the system's goal is to find the most appropriate response,rather than the most semantically similar one, this tendency results inlow-quality responses. We refer to this challenge as the echoing problem. Tomitigate this problem, we utilize a hard negative mining approach at thetraining stage. The evaluation shows that the resulting model reduces echoingand achieves better results in terms of Average Precision and Recall@N metrics,compared to the models trained without the proposed approach.

Avoiding Echo-Responses in a Retrieval-Based Conversation System