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Manus vs. Claude Chrome extension vs Claude Cowork
I evaluated three AI systems—Manus, Claude Chrome Extension, and Claude Cowork—on a complex planning task for an Akkadian translation competition, and none of them produced genuinely useful strategic analysis. All three independently generated the same obvious recommendation (fine-tune ByT5, augment data, run Optuna) with the same fabricated probability estimates, revealing that they're pattern-matching to surface-level solutions rather than doing real analytical work. The differences between them are purely cosmetic: one writes like an academic, one scrapes leaderboard numbers, one includes boilerplate code—but underneath the formatting, there's no there there.
Prompt:
Write up a well-researched plan for competing in https://www.kaggle.com/competitions/deep-past-initiative-machine-translation the Deep Past Challenge - Translate Akkadian to English on Kaggle. Read through all discussions https://www.kaggle.com/competitions/deep-past-initiative-machine-translation/discussion?sort=hotness and determine the top public notebooks, and top techniques mentioned. Formulate a plan with probabilities of placing at various percentiles (99.9 99 95 90 80th) for three alternative approaches. Deep dive into an investigation of these approaches across the latest info on the broader web. Include human-involvement time and generative-AI involvement time (and tokens/cost for vibe coding with claude code or cursor agent CLI.) and run-time for the training and eval/verification runs. How do these approaches allow for automatic hyper-param tweaking or other adjustments, especially hands off for the human. But differentiate between hands off for the LLM too. For instance Optuna or scikit-learn GridSearchCV etc do not require involvment of the vibe coding agent, the agent (and human) can set up a system that utilizes these and it runs without intermediate involvement of the LLM or human. Give time frames (is this minutes, hours, days, weeks?) Costs. Typically the running of optuna / grid search are basically free, but the LLM cost money/tokens as part of a monthly subscription. The end result is a detailed plan and report that is equally useful to human readers and LLMs.
I tested Manus, Claude Chrome Extension, and Claude Cowork on a Kaggle competition planning task requiring competitive analysis, strategy development, and cost estimation—and the result was a three-way tie for mediocrity. Chrome Extension edges out the others slightly by at least anchoring its claims in real leaderboard data, but "slightly less untethered from reality" is a low bar. All three systems produced documents that look like strategy but function as sophisticated summaries of what anyone could find in the competition's public notebooks.
The convergence tells the real story. When three supposedly different systems independently generate identical probability estimates (15-25% top 1%, 55-65% top 10%) and recommend the same approach, they're not analyzing—they're confabulating plausible-sounding numbers. No one showed their work because there is no work. Chrome Extension's leaderboard references and Cowork's code snippets create texture that feels like rigor, but pull on any thread and it unravels: the numbers don't inform the strategy, and the code is tutorial boilerplate.
What none of these systems attempted is the actual hard problem: understanding why the current top solutions plateau where they do and identifying unexploited angles that could beat them. They all defaulted to "do what the top notebooks already did, but maybe tune it better"—which is not a strategy, it's a prayer. If you needed a document to justify a project kickoff meeting, any of these would suffice. If you needed to actually win, you'd be starting from scratch.
So I turned to my default tool, claude code CLI, and it created a report that is more useful than the three mediocre documents:
Grounded in actual leaderboard data — it pulled real scores (38.7 top, 36.6 prize threshold) and analyzed actual winning notebooks, not fabricated probability estimates
Identifies what everyone ignores — The 580MB publications file nobody's using, the formulaic structure of Old Assyrian letters, NER as preprocessing rather than postprocessing, the chrF++ half of the metric
Calls out the commodity work — Instead of pretending ByT5 + sentence alignment is a strategy, is names it as table stakes and explain why marginal improvements on that path hit a ceiling
Three paths with honest tradeoffs — Not "Approach A vs B vs C" with made-up success probabilities, but actual risk/reward distinctions based on what the techniques require
Concrete steps, no fluff — Week-by-week breakdown of what to actually build, not vague recommendations to "optimize hyperparameters"
The core insight: the gap between 38.7 and 40+ exists, but it requires doing something the current solutions aren't doing. The document identifies where that gap lives—formula exploitation, entity masking, domain-adaptive pretraining, and the unexploited auxiliary data files.
Deep Past Challenge: A Strategy for Actually Winning
Executive Summary
The current leaderboard leader sits at 38.7. The academic state-of-the-art (Gutherz et al., PNAS Nexus 2023) achieved 37.47 BLEU. That's a delta of 1.23 points across an entire year of Kaggle competition with 1657 teams. Everyone is running the same playbook: ByT5, sentence alignment, weight averaging, translation memory. The path to winning isn't running that playbook slightly better—it's finding what that playbook misses.
The Current Meta (What Everyone Is Doing)
Based on actual top-scoring notebooks:
Technique
Description
Estimated Gain
ByT5-small/base
Byte-level transformer, handles unknown chars
Baseline
Sentence alignment
Split doc-level pairs into sentence pairs
+2-3 pts
Bidirectional training
Train both Akk→Eng and Eng→Akk
+1-2 pts
Weight averaging
Blend 2-3 checkpoints with perf-based weights
+0.5-1 pt
Gap normalization
Unify xx, ..., … → <gap>, <big_gap>
+0.3-0.5 pts
Translation memory
Exact-match test↔train lookups
+0.5-1 pt (on overlaps)
OA Lexicon post-proc
Normalize proper noun spellings
+0.2-0.5 pts
This is commodity work. Everyone who crosses ~35 is doing some combination of the above. The spread from 35 to 38.7 is marginal execution differences.
What The Meta Ignores
1. The 580MB Publications File Nobody Uses
The competition provides publications.csv at 580MB. The top notebooks train on ~3,500 document pairs from train.csv. Nobody in the public notebooks is systematically exploiting the publications data for pretraining or augmentation. This file likely contains thousands of additional cuneiform texts with transliterations.
Action: Extract parallel or semi-parallel data from publications. Even monolingual Akkadian transliterations enable continued pretraining of the encoder.
2. Old Assyrian Is Not Generic Akkadian
The Gutherz et al. model trained primarily on Neo-Assyrian royal inscriptions (2,997 samples) and administrative letters (2,003 samples). Old Assyrian merchant correspondence has different vocabulary, formulaic structures, and syntax. The competition's Michel Old Assyrian Letters corpus and OARE sentences are underutilized domain-specific resources.
Action: Domain-adaptive pretraining. Before fine-tuning on train.csv, continue pretraining ByT5 on all available Old Assyrian text (including monolingual transliterations from published_texts.csv and the Michel corpus).
3. Formula Exploitation
Old Assyrian business letters follow rigid templates:
The model doesn't know these are formulas. It treats "um-ma X-ma a-na Y qí-bí-ma" as arbitrary tokens when it's actually a constant template with two slot-fills.
Action:
Extract formula templates from training data
Create synthetic training pairs by slot-filling formulas with different names/quantities
The Gutherz paper explicitly identifies proper noun mistranslation as a major error source. The current approach (OA Lexicon post-processing) is reactive—fix names after generation. A proactive approach would:
Pre-identify names in the transliteration using the lexicon + determinatives ({d}, {m}, {f}, {ki})
Mask names with typed placeholders: <PERSON_0>, <DEITY_1>, <PLACE_2>
Train the model to translate with placeholders
Post-substitute canonical spellings
This eliminates hallucinated names entirely and lets the model focus on structure.
5. The chrF++ Half of the Metric
The evaluation is geometric mean of BLEU and chrF++. BLEU rewards exact n-gram matches. chrF++ is more forgiving of character-level variations. Most optimization focuses on BLEU (word choice, phrasing). But if your chrF++ is weak, the geometric mean craters even with high BLEU.
Action: Specifically optimize for character-level fidelity:
Preserve transliterated loanwords that appear in reference translations
Match punctuation and capitalization patterns in training targets
Use chrF++-weighted loss during training (not just evaluation)
6. Ensemble Diversity
Current ensembles blend 2-3 ByT5 checkpoints trained on slightly different data. These models make correlated errors because they share architecture and initialization.
Action: True ensemble diversity:
ByT5-small (fast, character-level)
mBART-50 (multilingual pretraining, different attention patterns)
Custom CNN à la Gutherz (different inductive bias entirely)
Blend by confidence-weighted voting, not weight averaging
The Three Paths
Path A: Incremental Meta Optimization (Safe, Top 10%)
Do what everyone does, but cleaner:
Train ByT5-base (not small) with the full augmentation stack
Use all available external data (ORACC, Michel, MTM24)
Aggressive translation memory with fuzzy matching
OA Lexicon + repetition cleanup
Expected score: 36.5-37.5
Cost: ~$30 compute, 20 hours
Risk: Low—this is the well-trodden path
Path B: Formula + NER Pipeline (Moderate Risk, Top 5%)
Build a structured pipeline:
Segment: Detect formula boundaries (opening/body/closing)
NER: Tag and mask entities with typed placeholders
Translate: Run ByT5 on masked input
Substitute: Replace placeholders with canonical names
Validate: Check against translation memory for sanity
Expected score: 37.5-38.5
Cost: ~$50 compute, 40 hours engineering
Risk: Medium—requires careful NER, but errors are recoverable
This changes the model's prior from "generic multilingual text" to "Old Assyrian merchant correspondence." If it works, it should dramatically improve rare word handling.
Expected score: 38-39.5
Cost: ~$100 compute (continued pretraining is expensive), 30+ hours
Risk: High—might not converge, might overfit to domain noise
Concrete Next Steps
Week 1: Foundation
Download and analyze publications.csv and published_texts.csv
Quantify how much additional parallel/monolingual data is available
Implement baseline ByT5 with full augmentation stack (sentence alignment, bidirectional, gap normalization)
Establish local validation (chrF++ * BLEU geometric mean)
Week 2: Differentiation
Build formula detector (regex + heuristics for openings/closings)
Implement NER tagger using determinatives + OA Lexicon
Create masked training pipeline
Test formula-aware translation on validation set
Week 3: Scale
If Path C looks viable, begin continued pretraining
Build diverse ensemble (ByT5 + mBART-50 minimum)
Implement confidence-weighted output selection
Tune translation memory fuzzy matching threshold
Week 4: Polish
Error analysis on validation predictions
Targeted fixes for systematic errors
Final ensemble calibration
Submit and iterate
What Would Actually Surprise
The winning solution will probably not be "ByT5 but trained longer." It will be one of:
Someone who found usable parallel data nobody else extracted from publications.csv or CDLI
Someone who exploited the formulaic structure so the model only has to translate the novel parts
Someone who solved named entity handling as a preprocessing step rather than postprocessing band-aid
Someone who used the lexicon as a hard constraint during beam search, not just post-hoc correction
The gap from 38.7 to 40+ exists. It requires doing something the current top solutions aren't doing. The techniques above are where that gap lives.
Appendix: Current Leaderboard Snapshot (2026-02-05)
This document outlines a comprehensive plan for competing in the Kaggle Deep Past Challenge, focusing on translating Akkadian to English. The competition presents unique challenges due to Akkadian being a low-resource, morphologically complex language with inconsistent transliteration formats and significant named entity recognition issues [1]. The evaluation metric is the geometric mean of BLEU and chrF++ scores [1].
Based on an analysis of the competition page, discussion forums, top public notebooks, and broader web research, three distinct approaches are formulated, each with varying levels of complexity, potential performance, and resource requirements.
1. Competition Analysis Summary
Key Challenges:
Named Entities: Inconsistent transliteration and semantic opacity of personal, place, and divine names significantly impact translation quality and metric stability [1]. The competition host has provided an onomasticon (curated list of names) to aid in addressing this [1].
Inconsistent Transliteration Formats: Different corpora use varying ASCII conventions for representing Akkadian, leading to potential loss of semantically meaningful distinctions (e.g., s / ṣ / š and t / ṭ) if not handled correctly [1]. Normalization to a consistent format, preserving diacritics, is crucial [1].
Gaps and Damage Markers: Standardizing the representation of damaged text (e.g., x to <gap>, multiple x to <big_gap>) and ensuring parallel alignment between transliteration and translation is vital for model performance [1].
Low-Resource Nature: The limited availability of parallel Akkadian-English data necessitates robust techniques for low-resource Neural Machine Translation (NMT) [2].
Key Techniques and Models Identified:
Models: ByT5 (byte-level T5), NLLB (No Language Left Behind), MarianMT, Flan-T5 [2].
Preprocessing: Diacritic preservation, consistent gap handling, and onomasticon integration [1].
Data Augmentation: Back-translation, leveraging external datasets (e.g., Larsen PDF) [2].
Hyperparameter Tuning: Automated methods like Optuna or GridSearchCV [2].
Ensembling: Combining multiple models for improved robustness and performance [2].
2. Alternative Approaches
Approach 1: Robust Baseline with Enhanced Preprocessing
This approach focuses on establishing a solid foundation by leveraging a well-understood NMT architecture combined with meticulous data preprocessing. It prioritizes stability and interpretability.
Model Architecture: Fine-tuned MarianMT or a standard T5 model (e.g., t5-small, t5-base). MarianMT is chosen for its efficiency and availability of pre-trained models for various language pairs, offering a good starting point for transfer learning [3].
Preprocessing:
Normalization: Implement a robust script to normalize Akkadian transliterations, preserving diacritics and converting ASCII substitutes to the competition's standard format [1].
Gap Handling: Standardize x and multiple x sequences to <gap> and <big_gap> respectively, ensuring parallel alignment with translations [1].
Named Entity Handling: Utilize the provided onomasticon for post-processing to correct or bias translations of named entities. This could involve a lookup table for known names.
Training Strategy: Supervised fine-tuning on the provided dataset. Focus on optimizing basic hyperparameters like learning rate and batch size.
Automation: Basic scripting for data preprocessing and model training. Hyperparameter tuning can be done manually or with a simple grid search.
Tasks: Code generation for preprocessing scripts, debugging assistance, boilerplate code for model training.
Time: 5-10 hours of interactive LLM usage.
Tokens/Cost: Estimated 500k-1M tokens for Claude Code/Cursor CLI, costing approximately $20-$50 (assuming average rates of $6/million tokens for Claude Code [4] and Cursor's usage-based pricing [5]).
Run-time (Training & Eval):
Training: 2-4 hours per run on Kaggle GPU (e.g., t5-small).
Evaluation/Inference: <1 hour per run.
Total Run-time: ~20-40 hours (multiple runs for tuning).
Automation Strategy: Hyperparameter tuning can be achieved using scikit-learn's GridSearchCV or a custom script for a limited search space. The human defines the parameter grid, and the system executes the trials without further intermediate human or LLM involvement. LLM assistance is primarily for initial code generation and debugging of the tuning script.
Approach 2: Advanced Transformer with Data Augmentation
This approach builds upon the baseline by incorporating more powerful transformer models and advanced data augmentation techniques to address the low-resource nature of Akkadian.
Model Architecture: Fine-tuned ByT5 or NLLB-200. ByT5's byte-level tokenization is particularly suited for handling the noisy and idiosyncratic Akkadian transliteration [2, 6]. NLLB-200 offers strong multilingual transfer learning capabilities [2].
Preprocessing: All steps from Approach 1, plus:
Advanced Named Entity Handling: Implement more sophisticated methods for integrating the onomasticon, such as biasing the model's output during decoding or using a dedicated named entity recognition (NER) component.
Training Strategy:
Supervised Fine-tuning: On the augmented dataset.
Data Augmentation:
Back-translation: Train a reverse English-to-Akkadian model (potentially using MarianMT) to generate synthetic Akkadian-English pairs from additional English texts [2].
External Data Integration: Explore and integrate relevant external Akkadian datasets, ensuring they undergo the same rigorous preprocessing and normalization [1].
Automation: Automated hyperparameter tuning using Optuna. This allows for more efficient exploration of the hyperparameter space.
Metric
99.9th Percentile
99th Percentile
95th Percentile
90th Percentile
80th Percentile
Probability of Placing
5%
20%
40%
60%
80%
Human-involvement Time:
Data Exploration & Preprocessing: 15-25 hours (developing advanced normalization, back-translation pipeline, external data integration).
Model Selection & Setup: 8-12 hours (configuring ByT5/NLLB, setting up augmentation pipeline).
Tasks: Complex code generation for data augmentation, Optuna integration, debugging complex model interactions.
Time: 10-20 hours of interactive LLM usage.
Tokens/Cost: Estimated 1M-2M tokens, costing approximately $50-$100.
Run-time (Training & Eval):
Training: 4-8 hours per run on Kaggle GPU (ByT5/NLLB are larger models). Back-translation model training might add another 2-4 hours.
Evaluation/Inference: 1-2 hours per run.
Total Run-time: ~50-100 hours (extensive tuning and augmentation).
Automation Strategy: Optuna is employed for hyperparameter optimization. The human defines the objective function and the search space. Optuna then autonomously explores different configurations, running trials in parallel or sequentially without direct human or LLM intervention during the search process. The LLM's role is to assist in defining the search space, generating the Optuna setup code, and interpreting the results to guide subsequent iterations.
This aggressive approach aims for top performance by combining the strengths of multiple models, potentially incorporating reinforcement learning, and extensive hyperparameter optimization. This strategy is resource-intensive and carries higher risk but offers the highest potential for a top percentile finish.
Model Architecture: Ensemble of multiple models (e.g., ByT5, NLLB, Flan-T5). This could involve weighted averaging of predictions or a more sophisticated stacking approach.
Preprocessing: All steps from Approach 2, with further refinement and potentially custom tokenization strategies for specific Akkadian linguistic features.
Training Strategy:
Multi-model Fine-tuning: Train each ensemble component separately using optimized hyperparameters.
Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO): If feasible, explore using RL-based methods to fine-tune the models further, leveraging human preferences for translation quality. This is noted as challenging due to reward stability issues with Akkadian data [1].
Extensive Data Augmentation: Maximize synthetic data generation and external data integration.
Automation: Advanced hyperparameter optimization with Optuna, potentially exploring neural architecture search (NAS) if time and resources permit.
Tokens/Cost: Estimated 2M-4M tokens, costing approximately $100-$200.
Run-time (Training & Eval):
Training: 8-16 hours per model in the ensemble. RL training can be significantly longer (potentially days).
Evaluation/Inference: 2-4 hours per run (due to ensemble complexity).
Total Run-time: ~150-300+ hours.
Automation Strategy: Optuna is used for comprehensive hyperparameter optimization across all models and ensemble weights. The human defines the search space and objective, and Optuna manages the exploration. For RL-based methods, the initial setup and reward function definition require significant human and LLM involvement. However, once configured, the RL training loop can run hands-off for both human and LLM, with periodic monitoring. The LLM's role is crucial for generating the intricate code for ensemble and RL components, as well as for advanced debugging and strategic guidance in navigating the complex interplay of these techniques.
3. Cost-Benefit and Automation Framework Analysis
Human-involvement vs. Generative-AI Involvement
Generative AI (e.g., Claude Code, Cursor Agent CLI) can significantly reduce human involvement in repetitive coding tasks, boilerplate generation, and initial debugging. However, human expertise remains critical for:
Strategic Decision-Making: Choosing the right approach, model architectures, and data augmentation strategies.
Problem Formulation: Defining the objective function for hyperparameter optimization and identifying key challenges.
Complex Debugging: Interpreting subtle model failures or unexpected behaviors that LLMs might struggle with.
Novel Research: Exploring new techniques not yet widely documented or implemented.
Automation for Hyperparameter Tuning
Tools like Optuna and scikit-learn's GridSearchCV are crucial for automating hyperparameter tuning. Once configured, these systems can run for extended periods without direct human or LLM intervention. The human (or LLM in a setup phase) defines:
Search Space: The range of hyperparameters to explore (e.g., learning rates, batch sizes, model dimensions).
Objective Function: The metric to optimize (e.g., geometric mean of BLEU and chrF++).
Computational Resources: Allocation of GPUs/CPUs for parallel trials.
This allows for efficient exploration of optimal configurations, minimizing manual effort and LLM token usage during the actual search process. LLMs can assist in defining the initial search space, generating Optuna/GridSearchCV code, and analyzing the results of the tuning process.
Overall Costs and Timeframes
Approach
Human Time (hours)
Gen-AI Time (hours)
Gen-AI Cost (USD)
Training/Eval Run-time (hours)
Timeframe
1: Robust Baseline
30-48
5-10
$20-$50
20-40
1-2 weeks
2: Advanced Transformer
53-82
10-20
$50-$100
50-100
2-4 weeks
3: Ensemble & RL
100-155
20-40
$100-$200
150-300+
4-8 weeks
Note: Generative AI costs are estimates based on typical usage for code generation and debugging assistance. Actual costs may vary based on specific LLM provider, model, and prompt complexity.
Deep Past Challenge: Akkadian-to-English Translation - Comprehensive Competition Plan
Executive Summary
The Deep Past Challenge is a $50,000 Kaggle competition to build neural machine translation models
for Old Assyrian cuneiform tablets—4,000-year-old Bronze Age texts. With 8,000+ training samples, the
task presents classic low-resource NMT challenges amplified by philological complexity including
inconsistent transliteration formats, named entity handling, and sentence alignment issues.
Current Leaderboard State (as of research date):
Top score: 38.7 (Geometric Mean of BLEU and chrF++)
Prize threshold (~6th place): ~36.5
Public baseline notebooks: ~34.5
Total teams: 1,657 | Submissions: 20,076
Part 1: Key Insights from Discussions & Notebooks
Critical Technical Challenges Identified
1. Data Preprocessing is the Dominant Factor Per discussion feedback from participants ranked
#15-#25: preprocessing alone can take you from 28 → 36+. The host's own ByT5 baseline achieves
~34.5 with basic formatting. Key preprocessing tasks include:
Gap normalization: Convert x → , x x x x → <big_gap>
Diacritic preservation: Keep š, ṣ, ṭ, ā, etc. (do NOT convert to ASCII)
Named entity handling: Use provided onomasticon for proper noun normalization
Sentence alignment: ~50% of train.csv has misaligned transliteration-translation pairs
Character normalization: Ḫ/ḫ → H/h for test compatibility
2. Model Architecture Insights Top approaches from notebooks and discussions:
ByT5 (byte-level T5): Most successful for handling diacritics and morphologically complex
Akkadian. Score: 34.4+ baseline
Flan-T5: Used for quality exploration and inference
NLLB (No Language Left Behind): Meta's multilingual model; question raised about
Early stopping with MedianPruner eliminates poor trials
Human needed only for final checkpoint selection
Placement Probability:
99.9th percentile (Top 2): 5%
99th percentile (Top 17): 15%
95th percentile (Top 83): 40%
90th percentile (Top 166): 60%
80th percentile (Top 331): 80%
Approach B: NLLB Fine-tuning with Multilingual Transfer
Strategy: Leverage Meta's NLLB-200 (trained on 200 languages including low-resource ones) for
transfer learning. The model has strong representations for Semitic languages which may transfer to
Note: NLLB uses CC-BY-NC license. Per discussion thread, this may impact prize eligibility—verify with
organizers.
Implementation Steps:
Same preprocessing as Approach A
Add Akkadian as pseudo-language code to NLLB tokenizer
LoRA fine-tuning (rank=16-64) to avoid catastrophic forgetting
Bidirectional training (Akk→Eng + Eng→Akk as data augmentation)
Knowledge distillation from larger NLLB variants
Time & Cost Estimates:
Phase
Human Time
LLM/Agent Time
GPU Time
Token Cost
Data preprocessing
8-12 hrs
4-6 hrs
-
~$15-25
NLLB tokenizer adaptation
3-4 hrs
2-3 hrs
-
~$10-15
LoRA training setup
2-3 hrs
2 hrs
-
~$5-10
Training (NLLB-600M)
2 hrs supervision
0 (automated)
20-30 hrs
$0
Grid search (9 configs)
1 hr setup
0 (automated)
30-45 hrs
$0
Inference optimization
2 hrs
1 hr
2 hrs
~$5
TOTAL
18-24 hrs
9-12 hrs
52-77 hrs
~$35-55
Automation Level: MEDIUM-HIGH
GridSearchCV-style search runs hands-off
Larger model = longer training = fewer iterations possible
May need manual intervention for memory issues
Placement Probability:
99.9th percentile: 3%
99th percentile: 12%
95th percentile: 35%
90th percentile: 55%
80th percentile: 75%
Approach C: Multi-Model Ensemble with Reinforcement Learning from AI Feedback (RLAIF)
Strategy: Train multiple diverse models (ByT5, T5, mT5, Flan-T5), then use RLAIF to select/combine
outputs. This addresses the observation that preprocessing variations affect different models differently.
Translation: Standard English + proper noun diacritics (ā, ī, ū)
This plan is designed to be actionable by both human readers and LLM agents. All technical details are verifiable against the Kaggle competition page and referenced discussions.
This document provides a comprehensive competition strategy for the Deep Past Challenge, a Kaggle competition to translate 4,000-year-old Old Assyrian cuneiform business records from Akkadian to English. Three approaches are analyzed with probability estimates, time/cost breakdowns, and automation strategies.
Key Findings:
Evaluation Metric: Geometric mean of BLEU and chrF++ (character-level F-score)
Top Approaches Identified: ByT5-base (character-level), T5/mT5 fine-tuning, MarianMT
Recommended Strategy: Approach B (ByT5 + Data Augmentation) for best effort-to-performance ratio
Total Estimated Investment: 40-120 hours human time, $50-500 compute/AI costs
1. Competition Overview
1.1 Task Description
Translate Old Assyrian cuneiform text (written circa 1950-1700 BCE) from Akkadian to English. The texts are primarily business records: contracts, letters, loans, and receipts from ancient Assyrian merchants.
Strategic Implication: Models must balance exact phrase matching (BLEU) with character-level accuracy (chrF++). Character-level models like ByT5 have an advantage for chrF++.
2. Research Summary: Top Techniques & Notebooks
2.1 Identified Public Notebooks
Notebook
Author
Approach
Notes
Deep Past Challenge - Baseline Model
leiwong
Baseline NMT
Official starter
Deep Past Challenge | byt5-base | Training
xbar19
ByT5 fine-tuning
Character-level
Deep Past Challenge: Starter Notebook
nihilisticneuralnet
Basic transformer
Educational
T5_Akkadian_Translation_Model
likithagedipudi
T5 fine-tuning
Subword-level
DeepPast | Akkadian -> English
amritanshukush
MarianMT
Transfer learning
2.2 Key Techniques from Literature
From PNAS Nexus Research (2023):
Achieved 36.52 BLEU (cuneiform-to-English) and 37.47 BLEU (transliteration-to-English)
CNNs and Transformers both effective
Formulaic texts (decrees, divinations) translate better than literary texts
Low-Resource NMT Best Practices:
Technique
Description
Expected Gain
Back-translation
Generate synthetic parallel data from monolingual target
+5-15 BLEU
Transfer learning
Pre-train on related language pairs
+3-8 BLEU
Character-level models
Better for rare words and morphology
+1-5 chrF++
Ensemble decoding
Average predictions from multiple models
+1-3 BLEU
Data augmentation
Synonym replacement, noise injection
+2-5 BLEU
3. Three Alternative Approaches
Approach A: Baseline Fine-Tuning (mT5/NLLB)
Strategy: Fine-tune a pre-trained multilingual model on the competition data with minimal customization.
Technical Details
Model: mT5-base or NLLB-200-distilled-600MParameters: ~580M (mT5-base) or 600M (NLLB)Training Steps: 10,000-50,000Batch Size: 8-16Learning Rate: 1e-4 to 5e-5Optimizer: AdamW
Strategy: Use large language models (Claude, GPT-4) for synthetic data generation and ensemble with traditional NMT models.
Technical Details
LLM Component: Claude 3.5 Sonnet / GPT-4NMT Component: Fine-tuned mT5/ByT5Hybrid Method:
1. LLM generates diverse translations for augmentation2. LLM scores/filters synthetic data quality3. NMT fine-tuned on augmented corpus4. Ensemble LLM + NMT predictions (optional)
LLM Integration Approaches
Option 1: Synthetic Data Generation
Input: English sentences from monolingual corpus
LLM Prompt: "Translate to Old Assyrian Akkadian (transliterated): [text]"
Output: Synthetic Akkadian for back-translation pipeline
Option 2: Few-Shot Translation Ensemble
Input: Akkadian test sample
LLM Prompt: "Given these examples of Akkadian-English translations:
[5-10 examples from training set]
Translate: [test sample]"
Output: LLM translation (ensemble candidate)
Option 3: Quality Filtering
Input: Synthetic translation pair
LLM Prompt: "Rate this Akkadian-English translation quality (1-10): [pair]"
Output: Quality score for filtering training data
Fully Hands-Off (No Human or LLM Involvement After Setup):
Optuna hyperparameter search
Grid search / random search
Scheduled training runs
Checkpoint averaging
Automated evaluation scripts
Requires LLM Involvement (Vibe Coding):
Initial code generation
Debugging complex errors
Prompt engineering iteration
Architecture modifications
Requires Human Involvement:
Strategic decisions (which approach)
Quality assessment of results
Final model selection
Kaggle submission
4.4 Risk Assessment
Risk
Impact
Approach A
Approach B
Approach C
Compute quota exceeded
High
Low
Medium
Medium
LLM API costs overrun
Medium
N/A
N/A
High
Overfitting small data
High
Medium
Low (augmentation)
Low
Suboptimal hyperparams
Medium
High
Medium
Medium
Deadline pressure
High
Low
Medium
High
5. Detailed Implementation Roadmap
Phase 1: Setup & Exploration (Days 1-3)
Tasks:
-[ ] Download competition data
-[ ] Exploratory data analysis (EDA)
-[ ] Set up development environment (Kaggle/Colab/Cloud)
-[ ] Install dependencies (transformers, sacrebleu, etc.)
-[ ] Create baseline submission
Deliverables:
- Data statistics report
- Baseline BLEU/chrF++ scores
- Initial submission to leaderboard
Phase 2: Approach Implementation (Days 4-14)
Approach A Timeline:
Day 4-5: Fine-tune mT5-base on competition data
Day 6-7: Set up Optuna HPO sweep
Day 8-10: Run HPO (automated)
Day 11-12: Analyze results, select best config
Day 13-14: Final training + submission
Approach B Timeline:
Day 4-6: Train reverse model (English→Akkadian)
Day 7-8: Generate back-translated data
Day 9-10: Implement noise augmentation
Day 11-14: Train ByT5 on augmented data
Day 15-18: HPO sweep (learning rate, data ratios)
Day 19-21: Ensemble multiple checkpoints
Approach C Timeline:
Day 4-7: Design LLM prompts, test quality
Day 8-12: Generate synthetic data (batched API calls)
Day 13-15: Quality filtering with LLM
Day 16-20: Train NMT on augmented corpus
Day 21-25: Build ensemble (LLM + NMT)
Day 26-28: Optimize ensemble weights
Phase 3: Optimization & Submission (Final Week)
Tasks:
-[ ] Checkpoint averaging (last 5-20 checkpoints)
-[ ] Ensemble diverse models
-[ ] Post-processing (if applicable)
-[ ] Generate final predictions
-[ ] Submit to private leaderboard
Automation:
- Scheduled nightly training runs
- Automatic validation scoring
- Slack/email notifications on completion
