![]() Second, we develop global deep learning system which controls the long-term structure of the dialogue. These indicate to the model at which part of the dialogue it is and which kind of coaching session it is carrying out. To alleviate this issue, we first propose to learn dialogue phase and scenario embeddings in the fine-tuning stage. However, since these only take as input a local dialogue history, a simple fine-tuning procedure is not capable of modeling the long-term dialogue strategies that appear in coaching sessions. We employ a transfer learning approach, pretraining GPT2 neural language models and fine-tuning them on our corpus. To this end, we gather a set of virtual coaching sessions through a Wizard of Oz platform, and apply state of the art Natural Language Processing techniques. Instead, we directly model the coaching strategy of professionals with end users. ![]() ![]() Unlike the majority of coaching, and in general, well-being related conversational agents that can be found in the literature, ours is not designed by hand-crafted rules. ![]() In this work we develop a fully data driven conversational agent capable of carrying out motivational coaching sessions in Spanish, French, Norwegian and English. ![]()
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