We Care: Multimodal Depression Detection and Knowledge Infused Mental Health Therapeutic Response Generation
Authors: Palash Moon, Pushpak Bhattacharyya


Reviewer paper summaries: The paper at hand extends a dataset called D-vlog on depression to make it have a better distribution of depression types. The authors mainly present a multimodal (audio, video, text) depression detection algorithm based on the TVLT model. They then prompt LLMs with psychotherapy-based techniques for generating responses for depressed patients. They carry out multiple experiments with ablations on both the detection and the generation models.
Summary of strengths: - Dataset statistics are clearly explained
- Depression detection model and its evaluation makes sense and is helpful as a tool for the community.
- Using multiple datasets comparing clinical vs real-life depression scenarios is a good evaluation mechanism
- The experiments seem technically sound, and the results are promising.
Summary of weaknesses: Most of my comments about the writing have not improved since the previous April cycle of the reviews. The only change I can see is the red Disclaimer in the abstract. I think that happened after the ethics review, so I would have expected some revisions in the writing of the main body, as I have mentioned before. I will re-write them in the comments section below.
Comments, suggestions and typos: - Bold subheadings in the intro are unnecessary
- In the intro, I did not understand the task setup. Maybe include what the tasks are: understanding and generation.
- It is not clear in the Datasets section that the new additional videos are from YouTube as well.
- Thank you for the change from stage 1/2 to detection and generation in the methods section. This was confusing before. Now it is better.
- Line 352: who is "the psychotherapist" again? Did you do a community outreach? Did the authors do a lit review on psychology work or did you collaborate with psychologists? 
- Table 5 could still benefit from additional explanation and a takeaway message. Why are these numbers important?
- Again, there is minimal change regarding my comments on the generation part of the work: The results and all the addition of generation, in general, read very out of place. The whole paper explains detection, and suddenly, there is this jump to generation.
Why suddenly change to LLaMA for generation instead of chatGPT?
Overall assessment scores: 2.5