Decida-small: 10-Pillar Automated Debate Adjudication Engine

Decida-small is an ultra-fast, 10-pillar automated debate adjudication engine tailored specifically for competitive Indonesian parliamentary and online debate tournaments.

Powered by fine-tuned IndoBERT, Sentence-Transformers, and local DeBERTa NLI Cross-Encoders, it evaluates multi-turn Affirmative vs. Opposition cases across 10 analytical dimensions, producing calibrated floating-point scores and constructive verbal feedback in approximately 4.2 seconds, without external LLM calls or cloud dependencies.

Author: Christopher Alberto Hamonangan

Core Highlights

Sub-5s Local Inference: Evaluates two teams across 6 dense multi-paragraph speeches in approximately 4.22 seconds on consumer GPU hardware.
Dedicated Indonesian NLP Stack: Utilizes fine-tuned IndoBERT (indobert-base-p1) for fallacy classification and semantic evaluation tuned to formal Indonesian argumentation register.
Zero-Daemon / Self-Contained: 100% Python and PyTorch. Eliminates external Ollama/HTTP dependencies, connection resets, and malformed JSON parsing timeouts.
Non-Judgmental Grounded Verbalizer: Combinatorial slot-filling synthesis engine featuring 250+ modular stylistic variants that quotes real evidentiary claims from the winning team to deliver positive adjudication feedback.

10 Evaluation Pillars & Scoring Topology

The total final score is computed as a weighted sum with a 100% aggregate weight:

Final Score = Σ (w_i × P_i), for i = 1 to 10

Pillar	Metric Dimension	Weight ((w_i))	Underlying Method / Model
P1	Kepadatan Semantik (Semantic Relevance)	10%	Dense cosine similarity vs. motion centroid via `all-MiniLM-L6-v2`
P2	Integritas Logika (Fallacy & Logic)	15%	Fine-tuned IndoBERT Sequence Classifier (`./model_indobert_fallacy`)
P3	Daya Respons/Bantahan (Rebuttal Clash)	10%	Batch local NLI cross-encoder (`contradiction` detection)
P4	Struktur A-R-E-L (Argument Structure)	10%	Structural causality, assertion grounding, and empirical regex patterns
P5	Pertarungan Dampak (Impact Weighing)	10%	Multi-layer trade-off and clash magnitude vector calculation
P6	Alur Wacana / Dinamika (Discourse Drift)	5%	Trajectory velocity & consecutive paragraph transition matrices
P7	Kepadatan Leksikal/Gaya (Lexical Register)	5%	Formal debate jargon index, lexical diversity (TTR), & structural depth
P8	Konsistensi Internal Tim (Team Consistency)	10%	Pairwise premise cross-check via NLI + semantic drift penalties
P9	Pertahanan & Komparasi (Resilience & Delta)	15%	70% premise defense ratio + 30% comparative delta vs. opposition
P10	Eksploitasi & Kepadatan (Targeting & Density)	10%	60% core attack targeting ratio + 40% propositional info density

Benchmark Run Sample

Mosi: "Dewan ini akan mewajibkan transisi 100% armada transportasi umum ke kendaraan listrik murni (EV) dalam waktu lima tahun ke depan."

Waktu Eksekusi: 4.22 detik

Metrik Pilar                  | TIM PRO | TIM KONTRA
----------------------------------------------------
P1: Kepadatan Semantik        |  82.65  |  80.92
P2: Integritas Logika         |  75.00  |  25.00
P3: Daya Respons/Bantahan     |  66.67  |  78.33
P4: Struktur A-R-E-L          |  52.50  |  59.17
P5: Pertarungan Dampak        |  57.85  |  83.38
P6: Alur Wacana / Dinamika    |  91.56  |  93.20
P7: Kepadatan Leksikal/Gaya   |  56.75  |  78.59
P8: Konsistensi Internal Tim  |  89.00  |  83.12
P9: Pertahanan & Komparasi    |  49.14  |  53.90
P10: Eksploitasi & Kepadatan  |  81.93  |  84.72
----------------------------------------------------
TOTAL SKOR AKHIR              |  69.10  |  67.39
====================================================

Pemenang: TIM AFIRMASI (PRO) (Margin: 1.71)

----------------------------------------------------
ULASAN ANALITIS DEWAN JURI (DECIDA-SMALL FEEDBACK):
----------------------------------------------------

Kemenangan dengan margin kompetitif 1.71 poin berhasil direngkuh oleh TIM AFIRMASI (PRO).

Penguasaan materi mendasar terbukti lewat susunan argumen yang utuh seperti terlihat saat menegaskan bahwa "Berdasarkan laporan Kementerian Lingkungan Hidup, lebih dari 65% polusi partikulat PM2.5 bersumber...", membangun alur berpikir yang meyakinkan.

Memasuki babak pembelaan dan penegasan posisi, alur penalaran tim mengalir secara organik (skor 89.00), di mana pembicara lanjutan bertindak sebagai akselerator kasus yang efektif.

Keunggulan paling menentukan hadir pada ketelitian berpikir tim, membentengi setiap kesimpulan dengan premis pendukung yang sah. Keberhasilan tim dalam menjaga beban pembuktian secara konsisten menjadi alasan fundamental di balik hasil ini.

====================================================

Repository Structure

decida-small/
│
├── pillar/
│   ├── __init__.py
│   ├── nli_client.py          # Local PyTorch batch NLI Cross-Encoder wrapper
│   ├── verbalizer.py          # Combinatorial grounded feedback generator (~0.001s)
│   ├── pillar1.py             # P1: Semantic density & motion relevance
│   ├── pillar2.py             # P2: Logical integrity (IndoBERT fallacy detection)
│   ├── pillar3.py             # P3: Rebuttal validity & direct clash
│   ├── pillar4.py             # P4: A-R-E-L structure analysis
│   ├── pillar5.py             # P5: Impact weighing & clash balance
│   ├── pillar6.py             # P6: Discourse trajectory & flow
│   ├── pillar7.py             # P7: Lexical density, register, & diversity
│   ├── pillar8.py             # P8: Multi-turn internal team consistency
│   ├── pillar9.py             # P9: Premise defense & comparative delta
│   └── pillar10.py            # P10: Strategic premise exploitation & density
│
├── model_indobert_fallacy/    # Fine-tuned weights (or pull via Hugging Face Hub)
├── model_minilm/              # Local sentence-transformer cache
│
├── .gitignoxre
├── LICENSE
├── README.md
├── requirements.txt
└── run_judger.py              # CLI driver script with batch execution benchmark

Installation & Setup

1. Prerequisites

Python 3.10 or higher
NVIDIA CUDA-compatible GPU (recommended for sub-5s performance) or modern multi-core CPU

2. Clone and Environment Setup

git clone https://github.com/USERNAME/decida-small.git
cd decida-small

python -m venv venv

# Windows (PowerShell)
.\venv\Scripts\Activate.ps1

# Linux / macOS
source venv/bin/activate

pip install --upgrade pip
pip install -r requirements.txt

3. Weights Configuration

Ensure the fine-tuned IndoBERT directory (./model_indobert_fallacy) and Sentence-Transformers directory (./model_minilm) exist locally, or update the model identifiers in pillar/pillar2.py and run_judger.py to point to your Hugging Face Model Hub repository:

# pillar/pillar2.py

MODEL_PATH = "USERNAME_HF/decida-indobert-fallacy"

Usage

Run the standard evaluation interface:

python run_judger.py

Mode 1 (Template): Automatically loads the built-in EV fleet transition motion along with 3 Affirmative and 3 Opposition speeches for immediate profiling.
Mode 2 (Live): Prompts for interactive speech-by-speech input with live multi-line terminal entry (--end to conclude debate turns).

Verbalizer Architecture

Unlike LLM prompt generators that can hallucinate or produce demoralizing critiques, pillar/verbalizer.py:

Computes the maximum comparative margin across all 10 pillars.
Extracts verbatim evidentiary phrases directly from the winning team's text.
Assembles a 4-paragraph formal critique using a slot-filling combination engine with over 250 verified positive rhetorical options.
Completes execution in less than 0.002 seconds.

Citation & License

This project is open-sourced under the MIT License.

@software{decida_small_2026,
  author       = {Christopher Alberto Hamonangan},
  title        = {Decida-small: 10-Pillar Automated Debate Adjudication Engine for Indonesian Debate},
  year         = {2026},
  publisher    = {Huggingface},
  journal      = {Huggingface Organization},
  howpublished = {\url{https://huggingface.co/BhinnekaAI/Decida-small}
}

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