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. |
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