Project Ideas

Conversational Question Generation with Emotional Regulation Strategy

Duration: One Semester
Lab: HCI/IC/EPFL
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
Abstract:

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.

Evaluation Baseline for Open-Domain Chatbots Based on Publicly Available Models and Apps

Duration: One Semester
Lab: HCI/IC/EPFL
Goals: Curate a benchmark dataset for testing novel evaluation metrics of conversational chatbots
Assistant: Ekaterina Svikhnushina (ekaterina DOT svikhnushina AT epfl DOT ch)
Student Name: Open
Keywords: Dataset curation; conversational chatbots; natural language processing
Abstract:

Evaluation of conversational chatbots is an open research problem within the NLP community. Previous studies tested various automatic metrics for a proxy of human evaluation of chatbot’s naturalness. While several popular automatic metrics correlated poorly with human judgment (Liu et al., 2016), perplexity demonstrated promising results (Adiwardana et al., 2020). However, the notion of naturalness in the aforementioned study did not include a set of essential human-like conversation attributes, e.g., entertainment or empathy, as suggested by the PEACE model (Svikhnushina and Pu, 2021). The aim of this project is to create a benchmark dataset of conversations with sufficient coverage of the PEACE constructs that could be further used for evaluation and comparison of different conversational models as well as testing of novel evaluation metrics.
The student is expected to:

  • Survey existing popular open-domain chatbots whose conversational responses could serve as a reasonable baseline
  • Create a benchmark dataset of conversations in a similar way as described in (Adiwardana et al., 2020)
  • Obtain human judgments for different conversational aspects for the curated data via crowdsourcing
Related Skills: Knowledge in natural language processing, data mining, and machine learning; strong analytical skills; programming skills (knowledge of Python is essential, basic web development skills 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.

Generating Reflections and Paraphrases out of Distress Stories in Mental Health Forums

Duration: One Semester
Lab: HCI/IC/EPFL
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: Open
Keywords: Entity-identification; Emotion recognition; Paraphrasing
Abstract:
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:
 
Distress story: 
  • “My mother is getting sick. She is alone in her village and only has one of my brother’s children staying with her. But I’m not sure the boy is taking good care of her. I am so worried because they are far from the hospital and he will not know what to do if she gets sicker.”

Reflection and paraphrase:

  • “It sounds like you are incredibly anxious at the moment, worrying about your mother’s health. You also seem to be concerned that the boy staying with her will be unable to look after her if necessary.”
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-orientationacknowledgementinterpretationunderstanding 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:
  • Apply natural language processing tools and methodologies to extract named entities and identify the characters involved in the story
  • Identify emotional reactions present in the story, towards whom they are directed and the causes behind
  • Generate reflections and paraphrases based on the above information
References:
- 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.

A Dialogue Dataset Containing Emotional Support for People in Distress

Duration: One Semester
Lab: HCI/IC/EPFL
Goals: Curate and analyse a large-scale dialogue dataset containing emotional support for people in distress, which can potentially be used to train a mental care giving chatbot.
Assistant: Kalpani Anuradha Welivita (kalpani DOT welivita AT epfl DOT ch)
Student Name: Chun-Hung Yeh (Taken)
Keywords: Emotional support; Web crawling; Natural language processing
Abstract:

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. Inability to regulate emotion in such episodes can potentially lead to self-destructive behavior such as substance abuse, self-harm or suicide. 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 to deal with emotional distress and help them regulate emotion. One of the major limitations in developing such a chatbot is the unavailability of a curated dialogue dataset containing emotional support. With this project, we aim to curate and analyse such a dataset having the potential to train and evaluate mental care giving chatbot that can support people in emotional distress.

The student is expected to:

  • Explore sources in the web from which we can collect large sets of conversations containing emotional support
  • Develop a web crawler to crawl potential sources in the web
  • Clean the conversations and develop a final dataset containing dialogues offering emotional support to people in distress
  • Perform analysis on the final dataset and come up with a preliminary taxonomy of response intents
Related Skills: Basic knowledge in natural language processing; Ability to code (preferably in Python); Experience in web crawling 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.

Discovering User Motivations and Experience of Open-Domain Chatbots Through App Reviews

Duration: One Semester
Lab: HCI/IC/EPFL
Goals: Identify the main drivers of people’s engagement with open-domain chatbots and explore various aspects of current interaction experience with them based on user reviews from Google Play market and Apple app store.
Assistant: Ekaterina Svikhnushina (ekaterina DOT svikhnushina AT epfl DOT ch)
Student Name: Alexandru Placinta (Taken)
Keywords: Qualitative research; social chatbots; motivations; remote experience evaluation
Abstract:

Due to recent advances in neural network-based language generation the area of open-domain chatbot development has become increasingly active. To ensure compelling user experience the design of conversational agents should meet eventual user goals and expectations. A number of studies in HCI community explored user motivations, needs, and perceptions of chatbots through a variety of methods, including surveying (Brandtzaeg, 2017), interviewing (Jain, 2018; Clark, 2019), diary studies (Muresan, 2019), and review analysis (Purington, 2017). However, previous works were either focusing on chatbots in general (also including task-oriented ones) or conducting a case study of a single specific agent. The aim of this project is to identify core user motivations and significant interaction experience aspects with open-domain chatbots based on elaborate analysis of reviews and ratings that users provided for a range of trending conversational apps on popular software distribution platforms.

The student is expected to:

  • Survey existing popular open-domain chatbots
  • Scrape user reviews and ratings of surveyed chatbots from Google Play market, Apple app store, or other platforms
  • Analyse the reviews using both ML-based automatic techniques (e.g., filtering methods, sentiment analysis, topic analysis, etc.) and qualitative and/or quantitative methods (e.g., coding, affinity diagramming, thematic analysis; correlation analysis, statistical analysis, etc.) to elicit main user motivations, experiences, and concerns of open-domain chatbots
Related Skills: Knowledge in Data Mining and/or Machine Learning; strong analytical skills; familiarity with natural language processing; statistical analysis basis
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.

Exploring Role of Interjections in Human Dialogs

Duration: One Semester
Lab: HCI/IC/EPFL
Goals: Create a taxonomy of English interjections with exhaustive lists of examples that could serve for enhancing the naturalness of chatbot’s responses.
Assistant: Ekaterina Svikhnushina (ekaterina DOT svikhnushina AT epfl DOT ch)
Student Name: Jean-Baptiste De La Broïse (Taken)
Keywords: Interjections; discourse analysis; taxonomy; natural language processing
Abstract:

Interjections are words and expressions that people use to communicate sudden reactions, feelings, and emotions. Most of the time we use interjections unconsciously and perceive them as an integral part of a human conversation. As naturalness is an important aspect for open-domain conversational agents, active research is being conducted on the role of interjections for chatbots. Earlier studies demonstrated that chatbots are evaluated as more natural and engaging when they use interjections in their responses (Marge, 2010; Cohn, 2019). However, previous works operated with limited lists of interjections and used custom heuristic rules and classifications to introduce them into agent’s utterances. The aim of this project is to create a robust taxonomy of interjections enriched with numerous examples so that it could be used reliably to enhance naturalness of chatbot’s responses.

The student is expected to:

  • Survey the Linguistics literature on the subject of interjections in human conversations, their form, position, and meanings
  • Come up with a fine-grained taxonomy of English interjections with exhaustive lists of examples for each class. Special attention should be paid to emotive interjections (e.g. positive surprise: Wow!, Whoa!; disgust: Yuck!, Ugh!; etc.)
  • Validate the taxonomy using the corpus of emotional human dialogs
Related Skills: Strong analytical skills; background and/or interest in discourse analysis; familiarity with natural language processing
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.

Increase Diversity in Chatbots’ Responses

Duration: One Semester
Lab: HCI/IC/EPFL
Goals: Build a chatbot that is able to generate diverse responses.
Assistant: Yubo Xie (yubo DOT xie AT epfl DOT ch)
Student Name: Taken
Keywords: Generative dialog model; natural language processing
Abstract:

By training a generative neural model on massive dialog data, a chatbot can already respond in a way that makes sense. However, sometimes the replies generated by chatbots are generic and dull (for example “I don’t know”), and lack some specificity according to the conversation context. One explanation is that these utterances appear more frequently in the training set, which leads to a higher probability of being generated. This project aims at increasing the diversity of the responses generated by chatbots, while still keeping them on the conversation topic.

The student is expected to:

  • do some literature review on the diversity issue of open-domain chatbots;
  • build a generative dialog model and implement a way to increase the diversity of the chatbot’s responses (for example ranking the beam search results according to mutual information);
  • evaluate your approach using both automatic metrics and human judgement.
Related Skills: Basic knowledge in natural language processing and neural networks
Suitable for: Master student. Interested student should contact Yubo Xie (yubo DOT xie AT epfl DOT ch) and Pearl Pu (pearl DOT pu AT epfl DOT ch) along with a copy of your CV as well as your transcript.