Bahasa Indonesia, Melayu, Filipino
Memuat data interpretasi naratif secara real-time...
The Full Story
Waktunya jadi detektif paling kacau sejagat raya di Disco Elysium tanpa perlu kamus di tangan. Telusuri kasus pembunuhan di Martinaise sambil berdebat sama otak lu sendiri dalam Bahasa Indonesia, Melayu, dan Filipino yang asik banget. Mod lokalisasi ini gila banget karena nerjemahin lebih dari 1 juta kata pake 8 tahap pemrosesan neural yang super cerdas. Harry ngomongnya berantakan, Kim tetep stay cool dan formal, sampe Cuno yang mulutnya gak difilter—semuanya dapet banget feel lokalnya. Ini bukan sekadar translate lurus, tapi adaptasi budaya yang bener-bener dipoles biar emosi dan slang-nya nyampe ke hati. Rasakan depresi dan indahnya Revachol dengan bahasa tongkrongan kita sendiri sekarang!
Experimental Build
Author's Notes
=== Audit Teknis & Semantik Lokalisasi DISCO ELYSIUM ===
1. SKALA LINGUISTIK & CAKUPAN
- Skala Proyek: Sekitar 1,067,777 kata diproses melalui alur neural 8-tahap.
- Cakupan Bahasa: Dukungan trilingual penuh untuk pasar Indonesia, Malaysia, dan Filipina.
- Status Kelengkapan: Indonesia: 99.0%, Malay: 99.2%, Filipino: 98.1%
- Analisis Variasi Leksikal: Source -> Density: 61.8% | Diversity: 2.4%, Indonesia -> Density: 70.1% | Diversity: 3.0%, Malay -> Density: 71.0% | Diversity: 2.2%, Filipino -> Density: 59.2% | Diversity: 3.1%
2. VALIDASI NEURAL & AKURASI
- Skor Keselarasan Semantik (Platt Score): Indonesia: 87%, Malay: 86%, Filipino: 85%
(Skor ini mengukur seberapa akurat terjemahan mempertahankan makna asli dari teks sumber.)
- Gaya Bahasa Karakter: Penyesuaian gaya (gaul, formal, santai) telah diterapkan pada 155 karakter unik.
- Pemulihan Struktur Otomatis (Tag Repair): 201 tag kode game telah dipulihkan secara presisi.
3. KAPABILITAS ENGINE
- Pipeline: Austronesian Localization System (Neural LoRA-Adaptive Architecture).
- Pengenalan Entitas: Ekstraksi penuh untuk terminologi spesifik game dan konstanta lore.
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Linguistic Analysis Report
Discourse analysis using Gemma embeddings. Classifies rhetorical register across the corpus to ensure tonal consistency with source narrative assets.
Emotional tone mapped via dot-product similarity between extracted dialog embeddings and predefined sentiment anchors using zero-shot semantic alignment.
DISCLOSURE: Profiling data generated algorithmically via zero-shot inference and semantic vector alignment. Represents AI interpretation of the dataset corpus, not explicit ground-truth statistics from the underlying game engine or internal metrics. Use as a heuristic guide for context mapping.
Cross-Lingual Quality Matrix
Semantic alignment quantified via Multilingual E5 Large Instruct (RoBERTa based) bitext mining. NER entities preserved using GLiNER heuristic extraction protocols to maintain terminological invariance.
* Sim = Cosine Similarity (Vector Space) · Density = Content/Total Tokens · Diversity = TTR (Type-Token Ratio) · "src" = Source Baseline · Named Entities enforced via GLiNER mining.
Heuristic markup verification utilizing multi-pass validation and correction to ensure syntactical integrity of control codes and visual tags.
Name
Narrative Profile
Associated Entities
Semantic Archetypes
NLP Pipeline Intelligence