Table-to-Dialog: Building Dialog Assistants to Chat With People on Behalf of You

Artificial Intelligence (AI) personal assistant has attracted much attention from both academia and industry. Almost all existing AI personal assistants serve as service terminals to chat with human users for certain tasks. We are instead interested in building AI personal assistants for a different yet important dialog scenario, where they chat with people to fulfill specific tasks on behalf of their human users. As the personal assistants are playing a requester role, instead of a service terminal role, the conversation goal becomes delivering or requesting information according to specific user requests precisely and efficiently. The challenge for the conversation policy is that all user requests must be delivered precisely, while the challenge for the response generation is that it's generally expected for machine generated responses to cover multiple information slots, either requesting or delivering, to make the conversation efficient. In this paper, we present Table-to-Dialogue, a novel approach to address the above challenges when building a requester role AI personal assistant. We employ an encoder-decoder network to learn explicit conversation policy, which generates the corresponding information slots based on the conversation context and the user request table. We further integrate a novel Multi-Slot Constrained Bi-directional Decoder (MS-CBD) into the above encoder-decoder network, to generate machine response according to the multiple slot values and their intermediate representations from the policy decoder. Different from the existing single direction text decoder approaches, MS-CBD leverage the bi-directional context of the response when generating it to enhance the semantic coherence. The experiments shows that our approach significantly outperform the state-of-the-art conversation approaches on automatic and human evaluation metrics.