Papers by Anuradha Welivita
Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums (2022.coling-1)
Copied to clipboard
| Challenge: | Existing therapeutic chatbots lack large-scale conversations between clients and trained counselors . prior work has found that social media platforms such as Reddit are used to vent distress and peers are seen to actively respond to such posts. |
| Approach: | They propose to use peer support platforms to scrape conversational data from Reddit to determine whether counselors' responses align with real therapeutic conversations. |
| Outcome: | The proposed method achieved 97% coverage out of 17.3K responses, meaning that out of 16.8K responses labeled with a moderate agreement. |
A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)
Copied to clipboard
| Challenge: | Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. |
| Approach: | They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents. |
| Outcome: | The proposed method improves the response quality of chatbots and makes them more controllable and interpretable. |
Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy (2023.findings-acl)
Copied to clipboard
| Challenge: | Lack of psychotherapeutic data makes it difficult to train chatbots . lack of mental health workers and stigma further demotivates people from seeking help. |
| Approach: | They propose to rephrase MI non-adherent responses into Advise with permission using a behavioral coding scheme to identify conforming and non-conforming responses. |
| Outcome: | The proposed rephrasers can be built with Blender and GPT3 to rephrase MI non-adherent Advise without permission responses into Adviser with permission. |
A Large-Scale Dataset for Empathetic Response Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing empathetic datasets are limited in size and cost due to the cost of manual labor. |
| Approach: | They propose to annotate 1M dialogues with 32 fine-grained emotions and eight empathetic response intents and the Neutral category using a silver dataset. |
| Outcome: | The proposed pipeline compares the quality of the proposed dataset with a state-of-the-art gold dataset using offline experiments and visual validation methods. |
A Taxonomy of Empathetic Questions in Social Dialogs (2022.acl-long)
Copied to clipboard
| Challenge: | Current dialog generation approaches do not model effective question-asking due to the lack of a taxonomy of questions and their purpose in social chitchat. |
| Approach: | They propose to model questions' ability to capture communicative acts and their emotion-regulation intents by annotating a large dataset with established labels. |
| Outcome: | The proposed model can be used to generate labels for the EmpatheticDialogues dataset and to further improve the existing models. |