Conversational Question Generation with Emotional Regulation Strategy (PAST)

April 17, 2021
Duration: One Semester
Goals: Build a conversational chatbot that generates questions with emotionally appropriate intents
Assistant: Ekaterina Svikhnushina (ekaterina DOT svikhnushina AT epfl DOT ch)
Student Name: Open
Keywords: Question generation; conversational chatbots; natural language processing

Questions constitute over 60% of human conversations and appear a key conversation driver. Yet, only few studies exist that focus on the development of conversational chatbots capable of asking meaningful questions (W. Wang et al., 2019; Y. Wang et al., 2018). The aim of this project is to take the next step towards automatic generation of appropriate and attentive questions leveraging the taxonomy of question types and intents which was recently established by the lab. The resulting model is expected to ask suitable questions given the emotional context of the dialog to approximate emotional regulation strategies occurring in human-human conversations.

The student is expected to:

  • Survey the related work on conversational question generation, taking note of employed methodological approaches and datasets
  • Build a question-generation model taking into account labeled question types and intents from the EmpatheticDialogues dataset (Rashkin et al., 2019)
  • Evaluate the model performance with automatic metrics and human judgment.
Related Skills: Ability to work independently; strong knowledge and skills in natural language processing and neural networks; strong analytical skills; knowledge of reinforcement learning is a plus.
Suitable for: Master student. Interested student should contact Ekaterina Svikhnushina (ekaterina DOT svikhnushina AT epfl DOT ch) and Pearl Pu (pearl DOT pu AT epfl DOT ch) along with a copy of your CV.