Keyword-Guided Neural Conversational Model

Authors

  • Peixiang Zhong Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU), Singapore Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU, Singapore
  • Yong Liu Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU), Singapore Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU, Singapore
  • Hao Wang Alibaba Group, China
  • Chunyan Miao Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU), Singapore Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU, Singapore

Keywords:

Conversational AI/Dialog Systems

Abstract

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.

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Published

2021-05-18

How to Cite

Zhong, P., Liu, Y., Wang, H., & Miao, C. (2021). Keyword-Guided Neural Conversational Model. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14568-14576. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17712

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III