{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.18529",
  "title": "Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning",
  "summary": "The method replaces the absence of any English-Syriac MT system with a Moses phrase-based SMT pipeline trained on a new 38,847-pair Biblical corpus with diacritic removal and BPE. It costs little compute but requires corpus construction, manual alignment by bilingual annotators, and per-configuration tuning of language model order, distortion limits, and OSM inclusion. Quality is limited to BLEU 23.54 with middling human scores, the Biblical domain will not transfer to general text, and neural or API-based translation will typically outperform phrase-based SMT where any neural coverage exists.",
  "rationale": "Unless the reader ships English-Syriac translation, this niche low-resource SMT baseline offers no production advantage over existing API-based MT, though its released corpus matters only to specialists in that language pair.",
  "technique": "Phrase-based SMT for English-Syriac translation",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18529",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.18529",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Best configuration achieves word-level BLEU 23.54, with human adequacy 3.42 and fluency 3.34 out of 5.",
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      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18529",
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    }
  ],
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      "dimension": "latency",
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    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}