Papers with response selection in

2 papers
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)

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Challenge: Existing methods to learn matching models for retrieval-based chatbots are lacking.
Approach: They propose a method that uses a sequence-to-sequence architecture model as a weak annotator to judge the matching degree of unlabeled pairs and performs learning with both the weak signals and the unlabed data.
Outcome: The proposed method improves on two public data sets on matching models on retrieval-based chatbots.
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)

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Challenge: Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based.
Approach: They propose a general co-teaching framework that learns matching models from noisy training data.
Outcome: The proposed learning framework can improve existing models on two public data sets.

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