---
language:
- hi
- en
tags:
- emotion-recognition
- code-mixing
- hinglish
- conversation
- semeval-2024
datasets:
- masac
metrics:
- f1
---

# HingRoBERTa ERC — E-MASAC

Fine-tuned [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta)
for Emotion Recognition in Conversation (ERC) on the SemEval-2024 Task 10 E-MASAC benchmark
(Hinglish TV dialogues from *Sarabhai vs. Sarabhai*).

## Performance
- **Test wF1: 0.4852** (HingRoBERTa + SVM soft ensemble)
- Phase 1 alone: wF1 = 0.3901
- +12.5 pts above SemEval LSTM baseline

## Architecture
Two-phase ensemble:
- Phase 1: Fine-tuned HingRoBERTa with Focal Loss + R-Drop, speaker embeddings, emotion-conditioned context
- Phase 2: SVM-RBF on 3,185-d features (Phase 1 embeddings + hand-crafted conversational features)

## Labels
`anger`, `contempt`, `disgust`, `fear`, `joy`, `neutral`, `sadness`, `surprise`

## Citation
Chakradhar Kotha, Konda Bhanu Sai Akhil, Venkata Siva Sai Krishna Pokuri
IIIT Hyderabad | SemEval-2024 Task 10