|Goals:||Application of natural language processing tools and methodologies to generate reflections and paraphrases of distress stories in Reddit|
|Assistant:||Kalpani Anuradha Welivita (kalpani DOT welivita AT epfl DOT ch)|
|Student Name:||Zheng Wang (Taken)|
|Keywords:||Entity-identification; Emotion recognition; Paraphrasing|
Most people suffer from emotional distress due to going through a significant life change, financial crisis, being a caregiver or due to various physical and mental health conditions. However, due to public and personal “stigma” associated with mental health, most people do not reach out for help. Even therapeutic consultations are limited and are not available 24/7 to support people when they are going through a traumatic episode. Therefore, it is important to assess the ability of AI-driven chatbots to help people deal with emotional distress and help them regulate emotion.
In recent times, more and more research is focused on how to generate controlled chatbot responses rather than generic responses produced by end-to-end response generation models. In such an era, it is of interest to look at ways and means of how practices in counselling could be incorporated into the chatbot response generation process. One of the most important dimensions of counselling is “reflection and paraphrasing” (Kagan and Evans, 1995). It lets the speaker know what you have understood and communicates empathy. This is achieved by the listener by both repeating and feeding a shorter version of their story back to the client as well as reflecting on their emotions. An example of a distress story and a generated reflection and paraphrase would be as follows:
Reflection and paraphrase:
Even though the above dimension is quite vaguely analysed in human-human conversations involving crisis counselling (Zhang and Danescu-Niculescu-Mizil, 2020) and empathetic conversation strategies (Welivita and Pu, 2020; Sharma et al., 2020; Pfeil and Zaphiris , 2007) (identified by different terms that refer to the same concept such as backward-orientation, acknowledgement, interpretation, understanding etc.), generation of detailed reflections and paraphrases that include contextual information, which makes the listener more heard of, has not been investigated. This project would address this concern by analysing a large-scale distress related dialogue dataset curated from a carefully selected subset of subreddits and investigating natural language processing tools and methodologies that could help us generate appropriate, contextually relevant reflections and paraphrases out of them, so that they could be incorporated in the chatbot response generation process.
The student is expected to:
- Kagan, C. and Evans, J., 1995. Counselling. In Professional Interpersonal Skills for Nurses (pp. 129-148). Springer, Boston, MA.
- Zhang, J. and Danescu-Niculescu-Mizil, C., 2020. Balancing objectives in counseling conversations: Advancing forwards or looking backwards. arXiv preprint arXiv:2005.04245.
- Welivita, A. and Pu, P., 2020. A Taxonomy of Empathetic Response Intents in Human Social Conversations. Proceedings of the 28th International Conference on Computational Linguistics.
- Sharma, A., Miner, A.S., Atkins, D.C. and Althoff, T., 2020. A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
- Pfeil, U. and Zaphiris, P., 2007, April. Patterns of empathy in online communication. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 919-928).
|Related Skills:||Knowledge in machine learning and natural language processing; Ability to code (preferably in Python); Experience in training and evaluating neural network models is preferable.|
|Suitable for:||Master student. Interested student should contact Anuradha Welivita (kalpani DOT welivita AT epfl DOT ch) and Pearl Pu (pearl DOT pu AT epfl DOT ch) along with a copy of your CV.|