---
title: "Knowledge-Infused Hierarchy-Aware Emotion — Srivastava 2026"
description: "Most emotion recognition work for mental health counseling targets English, even though many people in India express distress in Hinglish, a Romanized…"
authors: ["Aseem Srivastava", "Kushagra Mittal", "Anusha Tiwari", "Md. Shad Akhtar"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.226/"
doi: "10.63317/3xwvyj2py7r9"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/srivastava-2026-knowledge-infused-hierarchy-aware-emotion/"
---
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# Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations

**Authors:** Aseem Srivastava, Kushagra Mittal, Anusha Tiwari, [Md. Shad Akhtar](https://md.research.elamur.ai/free/en/authors/md-shad-akhtar/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.226/) · [PDF](https://aclanthology.org/2026.lrec-1.226.pdf) · [DOI 10.63317/3xwvyj2py7r9](https://doi.org/10.63317/3xwvyj2py7r9) · [OpenAlex](https://openalex.org/W7160015676)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Most emotion recognition work for mental health counseling targets English, even though many people in India express distress in Hinglish, a Romanized mix of Hindi and English. The authors fill this gap with **IndieMH**, which they describe as the first Hinglish counseling dialogue corpus. They built it from the English **HOPE** dataset. They first tried automatic pipelines: Google Translate followed by transliteration, and prompted ChatGPT. These produced unnatural output, so expert annotators translated the dialogues by hand. Patient turns were then labeled with 13 emotions grouped under 3 broad categories.

They also propose **Healer**, a classifier that combines conversational context with domain-specific mental health knowledge. Because the labels are heavily skewed, Healer is trained hierarchically. It first predicts the broad category, then uses a separate model within each category. Healer was compared with 11 baselines using accuracy, weighted-F1 and weighted-precision, and the authors report substantial gains. The resource supports emotion-aware virtual mental health assistants for code-mixed speakers.

## Key points
- **IndieMH** has 190 dyadic sessions and about 11.5K utterances: about 5.7K from patients (emotion-annotated) and 5.8K from therapists. The data and code are released at github.com/flamenlp/IndieMH.
- Patient utterances are split 70:20:10 into train/test/validation sets of 4118/1004/594 utterances, from 130/39/21 dialogues.
- The label distribution is strongly imbalanced. Neutral accounts for roughly 56% of patient utterances (3178 of 5716), followed by Sadness and Scared. This imbalance is the reason for the hierarchical 3-category → 13-label training scheme.
- The automatic translation attempts had specific problems:
  - speakers were often misgendered, with a male bias;
  - the pronouns *aap* and *tum* were used inconsistently;
  - some translations used overly formal Hindi, such as *mansik swasthya*.
- Healer is benchmarked against 11 baselines. The provided excerpt does not include the exact scores.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{srivastava2026knowledge,
  title = {{Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations}},
  author = {Srivastava, Aseem and Mittal, Kushagra and Tiwari, Anusha and Akhtar, Md. Shad},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3xwvyj2py7r9},
  url = {https://doi.org/10.63317/3xwvyj2py7r9}
}
```

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