Breakdown Detection in Negotiation Dialogues (Student Abstract)


  • Atsuki Yamaguchi The University of Sheffield
  • Katsuhide Fujita Tokyo University of Agriculture and Technology



In human-human negotiation, reaching a rational agreement can be difficult, and unfortunately, the negotiations sometimes break down because of conflicts of interests. If artificial intelligence can play a role in assisting with human-human negotiation, it can assist in avoiding negotiation breakdown, leading to a rational agreement. Therefore, this study focuses on end-to-end tasks for predicting the outcome of a negotiation dialogue in natural language. Our task is modeled using a gated recurrent unit and a pre-trained language model: BERT as the baseline. Experimental results demonstrate that the proposed tasks are feasible on two negotiation dialogue datasets, and that signs of a breakdown can be detected in the early stages using the baselines even if the models are used in a partial dialogue history.




How to Cite

Yamaguchi, A., & Fujita, K. (2020). Breakdown Detection in Negotiation Dialogues (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13969-13970.



Student Abstract Track