Yogeshvar Senthilkumar
Dr. Tony Chen *
"Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach" - Sharma et al., 2021
"A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support" - Sharma et al., 2021
"DYME: A Dynamic Metric for Dialog Modeling Learned from Human Conversations" - von Unold et al., 2021
"Text Coherence Analysis Based on Deep Neural Network" - Cui et al. - 2017
I feel really sad for you
Everything will be fine
vs
Emotional Reactions
This must be terrifying
Interpretations
I understand how you feel
vs
What happened?
Explorations
Are you alone right now?
vs
Uses EPIOME in metrics
Utterance length
Diversity
Utterance Repetition
What's the performance of BayMax against the various different Language Models on Empathetic Rewriting Tasks?
Does BayMax produce Higher Empathetic Responses?
Produce Empathetic responses and generate candidate response for rewriting.
Comparing the responses produced by BayMax vs Other Language models to evaluate.
Protector & Friend are some Proof of Concepts applications built based on BayMax
so far
Comparing the responses produced by BayMax vs Other Language models to evaluate.
For training/fine-tuning huge parameterised language models requires a lot of data. We use Reddit Data which is publicly available
Wide Range of Data available in the Language Model will not be producing better results for a specific domain. We fine-tune the GPT-2 Model with EmpatheticDialogues
User: "My diet becomes f**ked when I get depressed... I can’t control myself I eat so much when I’m de- pressed."
BayMax: "The only way to stop is a hard and fast recovery, but you gotta keep pushing through it."
GPT-2 with Fine Tuning
Responses from BayMax (GPT-2) with fine tuning
Produce Empathetic responses and generate candidate response for rewriting.
(Better understanding of architecture on next page)
Protector & Friend are some Proof of Concepts applications built based on BayMax
Comparing the responses produced by BayMax vs Other Language models to evaluate.
Seeker: When my current girlfriend said yes to dating me.
Baymax: It sounds like you're feeling really happy and excited. It's great to hear that your girlfriend said yes and that you're in a good place in your relationship.
PARTNER: That is amazing! Are you happy and excited?
BART: oh nice! I hope you are happy.
T5: Oh wow, that's really nice of her.
I don't feel so great today, I need a friend to talk to.
Seeker:
I am here for you. Let's talk about what's been on your mind and see how we can make you feel better.
Baymax:
Why don't you talk to them?
T5:
What's wrong with you?
BART:
I'm here for you. What do you want to talk about? Like School or Work?
PARTNER:
Comparing the responses produced by BayMax vs Other Language models to evaluate.
| Model | Accuracy Empathy | Macro f1 Empathy | Accuracy Rationale | Macro f1 Rationale |
|---|---|---|---|---|
| BAYMAX | 0.894 | 0.722 | 0.755 | 0.754 |
| PARTNER | 0.903 | 0.751 | 0.754 | 0.782 |
EX, IP Rationales
I don't feel so great today, I need a friend to talk to.
Seeker:
I am here for you. Let's talk about what's been on your mind and see how we can make you feel better.
Baymax:
Why don't you talk to them?
T5:
What's wrong with you?
BART:
I'm here for you. What do you want to talk about? Like School or Work?
PARNTER:
| Edit | BLEU | Distinct | Coherence | Empathy |
|---|
| 0.49 | 0.139 | 10.95 | 0.658 | 1.49 |
|---|
| 0.599 | 0.120 | 10.83 | 0.137 | 0.49 |
|---|
| 0.66 | 0.135 | 10.74 | 0.227 | 0.38 |
|---|
| 0.538 | 0.225 | 10.933 | 0.536 | 1.27 |
|---|