Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The authors describe a graph neural network model to predict severity of depression from available text.Application of this method to text data itself is not unique but the clinical context is relevant and important.Many a times in clinical scenarios we do not have access to audio-video data and that's where methods such as these are important utilizing transcribed text.Comparison with prior models is relevant and important and the improvement somewhat significant.
main_review: Strengths:
- Interesting application of GNN in a clinical field where there is a great need of such applications.Clear clinical benefit.
-Shows superior performance compared to prior methods with limited text data only.

Weakness:
- Details of prediction are missing.Is the performance better in patients with severe depression vs mild.While average and aggregated data results are presented, details of underperformance and opportinities for improvement could be identified in subsequent work.
-Unclear why the performance is better compared to multimodal data approaches.Details of the comparison might be worth investigating.
summary_of_the_review: This paper has a very practical clinical context and if generalizable has good opportunity to for application.It is unclear why the performance is superior to other multimodal methods which should be investigated more with details of performance variability.Overall good work.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a schema-based GNN method to measure the severity of depression. To gain a global representation of each word, the proposed method constructs word nodes and uses schema structure to capture the context-level information. The main contribution of this paper is the introduction of the schema encoder. The experimental results show the superiority of schema GNN over other SOTA models.
main_review: +Ves
+ The idea of using schema GNN is interesting for understanding depressive symptoms.
+ The paper is a nice effort with visualizations and nice structure, the ablation studies have been well motivated by pointing out the difference of using different operations. 
+ The results indicate the effectiveness of the proposed method.

-Concerns

-Considering the size of dataset, I deem that, the authors should do more experiments on multiple other datasets about depression detection to prove the good performance of the proposed method.
-The related works are insufficient. There are many studies that worked on DAIC-dataset, such as: 
1. Mallol-Ragolta A, Zhao Z, Stappen L, et al. A hierarchical attention network-based approach for depression detection from transcribed clinical interviews[J]. 2019.
2. Lam G, Dongyan H, Lin W. Context-aware deep learning for multi-modal depression detection[C]//ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019: 3946-3950.
3. Ray A, Kumar S, Reddy R, et al. Multi-level attention network using text, audio and video for depression prediction[C]//Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop. 2019: 81-88.
4. Zhang Y, Wang Y, Wang X, et al. Text-based Decision Fusion Model for Detecting Depression[C]//2020 2nd Symposium on Signal Processing Systems. 2020: 101-106.
Dinkel H, Wu M, Yu K. Text-based depression detection on sparse data[J]. arXiv preprint arXiv:1904.05154, 2019.
5.Solieman H, Pustozerov E A. The Detection of Depression Using Multimodal Models Based on Text and Voice Quality Features[C]//2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021: 1843-1848.
6. Lin L, Chen X, Shen Y, et al. Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model[J]. Applied Sciences, 2020, 10(23): 8701.
7. Arseniev-Koehler A, Mozgai S, Scherer S. What type of happiness are you looking for? - A closer look at detecting mental health from language[C]//Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 2018: 1-12.

-Because the authors said the method could help us understand depression, the paper should contain some interpretability analysis using visual figures or examples of results, which can help us to know which words the model mainly focuses on and the learning process.

-In Figure 2, Is there a threshold to check the neighbor word which is encountered?


Minor comments: 

*The authors should add some references about schema structure.

*The reference of the READOUT function should be added.

*The figures are a bit blurry, please replace them with vector diagrams.

summary_of_the_review: The idea of using schema-based GNN is interesting and have good performance on DAIC dataset. The paper is well written, but many related works are missing. In addition, the authors should add the interpretability analysis.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper tries to leverage graph neural networks to improve the performance of measuring the severity of depressive symptoms. Experiments are conducted on a benchmark dataset.
main_review: Strengths:

-The motivation of the paper is clear. And the overall framework is introduced well. The paper is easy to read.

Weaknesses:

-Graph neural networks have been widely leveraged to improve text analysis. The novelty of the method is limited.

-The experimental analysis is insufficient. Comparisons with recent works on measuring depressive symptom severity are not involved, such as [1], [2], [3]. 

[1] Muzammel M, Salam H, Othmani A. End-to-End Multimodal Clinical Depression Recognition using Deep Neural Networks: A comparative Analysis[J]. Computer Methods and Programs in Biomedicine, 2021: 106433.

[2] Yang L, Jiang D, Sahli H. Feature augmenting networks for improving depression severity estimation from speech signals[J]. IEEE Access, 2020, 8: 24033-24045.

[3] Lin L, Chen X, Shen Y, et al. Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model[J]. Applied Sciences, 2020, 10(23): 8701.

summary_of_the_review: The paper is presented clearly. But the method is not novel. The comparisons with recent works are also not involved.
review_title: null

Meta-review: This paper presents a graph neural network model to predict the severity of depression symptoms from text. It proposes to construct a graph with word nodes and use schema structure to capture the context information in the text. A schema encoder is introduced for modeling the constructed graphs.

Strength:
- Interesting application domain and clear motivation
- Experiment results demonstrate the effectiveness of the method
- Paper writing is clear and easy to follow

Weakness:
- Technical novelty of the method is limited
- Experiment comparison with some recent work is missing
- More in-depth analysis on the method are needed
- Some details of the method pipeline require further elaboration
Decision: Accept (Poster)