SemEval 2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays

📄 Abstract

The Pytorch version of our SemEval-2026 Task 2 submissions is found within this repository and addresses three major issues in affective computing: state prediction, change forecasting and long-term trajectory prediction.

We utilize efficient "hybrid" architectures; specifically the Siamese Network ("Bifurcated Leviathan"), and custom loss functions (CCC (Concordance Correlation Coefficient)) to prevent regression to the mean, due to resource limitations associated with consumer-grade hardware (8GB VRAM).

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📂 Repository Structure

├── paper/
│   ├── SemEval_Paper.pdf
├── src/                         # Source code for training and inference
│   ├── subtask1_longitudinal.py 
│   ├── subtask2a_forecasting.py 
│   └── subtask2b_disposition.py
├── LICENSE
├── predictions/                 # Output CSVs for submission
├── splits_subtask1/             # Generated automatically
├── splits_subtask2a/            # Generated automatically
├── splits_subtask2b/            # Generated automatically
├── train_subtask1.csv           # Raw Dataset
├── train_subtask2a.csv          # Raw Dataset
├── train_subtask2b.csv          # Raw Dataset (Main file for Subtask 2B)
├── train_subtask2b_detailed.csv
├── train_subtask2b_user_disposition_change.csv
├── README.md                    # Project documentation
└── requirements.txt             # Python dependencies

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🎯 Task Definitions & Methodologies

1. Subtask 1: Longitudinal Affect Assessment

The Task: Given a chronological sequence of m texts $e_1, e_2, \dots, e_m$, the model must produce Valence & Arousal (V&A) predictions for each text: $(v_1, a_1), \dots, (v_m, a_m)$.

• Constraint: The test split includes Unseen Users (zero-shot generalization) and Seen Users (temporal tracking).

Our Solution: The Hybrid Early-Fusion Model

• Architecture: distilbert-base-uncased + BiLSTM.
• Innovation: Instead of relying solely on text, we implement Early Fusion. An explicit User Embedding (dim=32) is concatenated with the text embedding before temporal processing. This allows the LSTM to condition its memory on the specific user identity.
• Inference: Uses a custom SlidingWindowDataset to prevent "context starvation" (forgetting history) during testing.

2. Subtask 2A: Forecasting State Changes

The Task: Given a sequence of texts and their V&A scores up to time $t$, predict the immediate next-step change in Valence and Arousal: $$ \Delta_1 = v_{t+1} - v_t $$

Our Solution: The State-Aware Projector

• The Problem: "The Drowning Problem." High-dimensional text vectors (768-dim) overwhelm low-dimensional scalar inputs (current state $v_t, a_t$).
• The Fix: A Projection MLP boosts the scalar state features into a higher-dimensional space (64-dim) before fusion.
• Loss Function: We replaced MSE (Mean Squared Error) with CCC Loss.
  • Observation: MSE caused the model to predict "zero change" (flatline) to minimize error.
  • Result: CCC forces the model to match the variance of the trajectory, improving correlation from 0.39 to 0.64.

3. Subtask 2B: Dispositional (Long-Term) Change

The Task: Predict the change between the mean observed affect (past) and the mean future affect (future): $$ \Delta_{\text{avg}} = \text{avg}(v_{t+1:n}) - \text{avg}(v_{1:t}) $$

Our Solution: The "Bifurcated Leviathan"

• Architecture: A Siamese Network with a shared deberta-v3-large backbone.
• Sampling: Implements a "Head-Tail" protocol, sampling the first 16 essays (Head) and last 16 essays (Tail) to model long-term drift.
• Residual Learning: We inject the arithmetic difference of the raw scores ("Naive Math") into the final layer. The network learns to refine this statistical trend rather than deriving it from scratch.
• Bifurcation: The network splits immediately after the backbone into separate Valence and Arousal heads to prevent noisy Arousal gradients from disrupting Valence learning.

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📊 Results & Performance

Task	Metric	Score (Pearson $r$)	Key Insight
Subtask 1	Valence (Seen)	0.7026	User Embeddings are critical for known users.
Subtask 1	Arousal (Seen)	0.5186	Arousal is notoriously harder to model than Valence using text.
Subtask 2A	Avg Correlation	0.64	CCC Loss outperformed MSE by ~27%.
Subtask 2B	Valence Change	0.7031	Residual learning ("Naive Math") prevents scale collapse.

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🚀 Setup & Usage

Prerequisites

• Python 3.10+
• NVIDIA GPU (Minimum 8GB VRAM recommended for training)

Installation

# Clone the repository
git clone [https://github.com/YourUsername/SemEval-2026-Task2.git](https://github.com/YourUsername/SemEval-2026-Task2.git)
cd SemEval-2026-Task2

# Install dependencies
pip install -r requirements.txt

1. Subtask 1:

   python src/subtask1_longitudinal.py

   This script handles the "Seen/Unseen" user split automatically.

2. Subtask 2A:

   python src/subtask2a_forecasting.py

   This executes the V5 architecture (DeBERTa + Projection) using CCC Loss to replicate our best results.

3. Subtask 2B:

   python src/subtask2b_disposition.py

   This implements the "Bifurcated Leviathan" model with Head-Tail sampling.

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🤝 Acknowledgements & Credits

Originality Statement

The architectures presented here (including the "Leviathan" Siamese network and the Hybrid LSTM-Fusion) are original contributions developed for this competition.

External Resources

We gratefully acknowledge the open-source community. Specifically, initial data processing patterns and file handling structures were informed by the work of:

• ThickHedgehog (2025): Deep-Learning-project-SemEval-2026-Task-2. Available at: GitHub.

Note: While preprocessing logic was inspired by the above, the modeling strategies (Early vs. Late Fusion, usage of LSTM for Subtask 1, and CCC optimization) differ significantly in implementation and topology.

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📜 Citation

If you use this code or our findings in your research, please cite:

@inproceedings{jumakhan2026longitudinal,
  title={Longitudinal Affective Forecasting: Architectures for Generalization, State Change, and Trajectory Prediction},
  author={Jumakhan, Haseebullah and Assad, Soud and Ahmad, Seyed Abdullah},
  booktitle={Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)},
  year={2026}
}