// demo-project.jsx — A canned MQM result so reviewers can land on the // Results screen without running a real assessment. Modeled on the // ATC-DL paper the user sent as a reference (RU → EN, ML domain) but // with English labels per the user's explicit request. window.__APE_DEMO_PROJECT = { project: { id: 'p_demo_atcdl', name: 'ATC-DL paper (RU \u2192 EN)', sourceFile: 'atc-dl_source.docx', targetFile: 'atc-dl_translation.docx', sourceText: '(demo)', targetText: '(demo)', glossary: '', }, data: { source_language: 'Russian', source_language_code: 'ru', target_language: 'English', target_language_code: 'en', domain: 'Machine learning research paper \u2014 distributed optimization', source_word_count: 3796, verdict: 'High-quality translation of a research paper. Terminology is rendered consistently at a near-specialist level and the technical exposition is faithful. Main risks are a small set of meaning-altering additions (notably an inserted \u201cdegradation\u201d framing in the abstract and a self-contradictory clause about DissDL speedup), plus a systemic table-numbering conflict introduced by the translator. Style and register are otherwise idiomatic and publication-ready after light post-editing.', segments: [ { id: 1, section: 'Title', source: '\u0410\u0434\u0430\u043f\u0442\u0438\u0432\u043d\u043e\u0435 \u0434\u0435\u0446\u0435\u043d\u0442\u0440\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441 \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u044b\u043c \u0443\u0447\u0451\u0442\u043e\u043c \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u043e\u0439 \u0433\u0435\u0442\u0435\u0440\u043e\u0433\u0435\u043d\u043d\u043e\u0441\u0442\u0438.', target: 'Adaptive Decentralized Deep Learning with Joint Consideration of Statistical and System Heterogeneity.', corrected: 'Adaptive Decentralized Deep Learning with Joint Consideration of Statistical and System Heterogeneity.' }, { id: 2, section: 'Abstract', source: '\u0414\u0435\u0446\u0435\u043d\u0442\u0440\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u043e\u0431\u0443\u0447\u0430\u0442\u044c \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438 \u043d\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u0445 \u043f\u043e \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0443 \u0443\u0437\u043b\u043e\u0432.', target: 'Decentralized deep learning enables the training of neural networks on data distributed across multiple nodes.', corrected: 'Decentralized deep learning enables the training of neural networks on data distributed across multiple nodes.' }, { id: 3, section: 'Abstract', source: '\u0418\u0445 \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435 \u0434\u0430\u0451\u0442 \u0441\u0432\u0435\u0440\u0445\u0430\u0434\u0434\u0438\u0442\u0438\u0432\u043d\u044b\u0439 \u0432\u044b\u0438\u0433\u0440\u044b\u0448 \u0432 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0441\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438.', target: 'Their joint action yields a super-additive gain in convergence speed without degradation of accuracy.', corrected: 'Their joint action yields a super-additive gain in convergence speed.' }, { id: 4, section: '\u00a71.1', source: '\u0420\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u043e\u0435 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0439 \u043f\u0430\u0440\u0430\u0434\u0438\u0433\u043c\u043e\u0439 \u0434\u043b\u044f \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0438 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439.', target: 'Distributed deep learning has emerged as a foundational paradigm for scaling neural network training.', corrected: 'Distributed deep learning is a key paradigm for scaling neural network training.' }, { id: 5, section: '\u00a71.1', source: '\u0421\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043a\u043e\u043b\u044c\u0446\u0435\u0432\u0430\u044f \u0442\u043e\u043f\u043e\u043b\u043e\u0433\u0438\u044f \u0432\u044b\u043d\u0443\u0436\u0434\u0435\u043d\u0430 \u0436\u0434\u0430\u0442\u044c \u0441\u0430\u043c\u044b\u0445 \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u044b\u0445 \u0443\u0437\u043b\u043e\u0432 (T4), \u0442\u0435\u0440\u044f\u044f \u0434\u043e 60% wall-clock \u0432\u0440\u0435\u043c\u0435\u043d\u0438.', target: 'A static ring topology must wait for the slowest nodes (T4), wasting up to 60% of wall-clock time.', corrected: 'A static ring topology must wait for the slowest nodes (T4), losing up to 60% of wall-clock time.' }, { id: 6, section: '\u00a71.2 Table', source: '\u0423\u0447\u0451\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u043e\u0439 \u0433\u0435\u0442\u0435\u0440.', target: 'System Heterogeneity', corrected: 'System Heterogeneity Awareness' }, { id: 7, section: '\u00a71.2 Table caption', source: '[\u043d\u0435\u0442 \u043f\u043e\u0434\u043f\u0438\u0441\u0438 \u0432 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0435]', target: 'Table 1: Comparison with related methods', corrected: '[no caption in source]' }, { id: 8, section: '\u00a73.2', source: '\u03c4max\u00b7\u03c3\u00b2: \u0437\u0430\u0434\u0435\u0440\u0436\u043a\u0430 \u043e\u0442 \u0441\u0442\u0440\u044d\u0433\u0433\u043b\u0435\u0440\u043e\u0432 (\u03c4max) \u0443\u0441\u0438\u043b\u0438\u0432\u0430\u0435\u0442 \u0441\u0442\u043e\u0445\u0430\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0448\u0443\u043c.', target: '\u03c4max\u00b7\u03c3\u00b2: straggler-induced delay (\u03c4max) amplifies stochastic gradient noise.', corrected: '\u03c4max\u00b7\u03c3\u00b2: straggler-induced delay (\u03c4max) amplifies stochastic noise.' }, { id: 9, section: '\u00a75.4', source: '\u0428\u0430\u0433 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f | \u03b7 | \u0423\u0434\u043e\u0432\u043b\u0435\u0442\u0432\u043e\u0440\u044f\u0435\u0442 \u03b7\u22641/(4LT); \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 grid search.', target: 'Learning rate | \u03b7 | Satisfies \u03b7\u22641/(4LT); selected via grid search.', corrected: 'Learning rate | \u03b7 | Satisfies \u03b7\u22641/(4LT); in practice, selected via grid search.' }, { id: 10, section: '\u00a76.1', source: '\u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u043a\u0430\u043b\u0438\u0431\u0440\u043e\u0432\u0430\u043d\u044b \u043f\u043e \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u043c \u0438\u0437\u043c\u0435\u0440\u0435\u043d\u0438\u044f\u043c \u0438\u0437 [10] (\u0422\u0430\u0431\u043b\u0438\u0446\u0430 3, \u043a\u043b\u0430\u0441\u0442\u0435\u0440 \u0441\u043c\u0435\u0448\u0430\u043d\u043d\u044b\u0445 GPU).', target: 'LogNormal and Pareto parameters were fitted from real measurements reported in [10] (Table 3, mixed-GPU cluster).', corrected: 'LogNormal and Pareto parameters were calibrated against real measurements reported in [10] (Table 3, mixed-GPU cluster).' }, { id: 11, section: '\u00a76.1', source: '\u0414\u043b\u044f \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0441\u0442\u0438: (\u0430) \u0432 \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0445 \u0442\u0430\u0431\u043b\u0438\u0446\u0430\u0445 1\u20132 \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0430 \u0441\u0442\u0440\u043e\u043a\u0430 \u00abATC-DL (sync, doubly stoch.)\u00bb', target: 'For correctness: (a) a row "ATC-DL (sync, doubly stoch.)" is added to the main Tables 1\u20132', corrected: 'For fairness: (a) a row "ATC-DL (sync, doubly stoch.)" is added to the main Tables 1\u20132' }, { id: 12, section: '\u00a76.4 Table 1', source: '\u0422\u0430\u0431\u043b\u0438\u0446\u0430 1. Top-1 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c (%), n=32, 300 \u044d\u043f\u043e\u0445 (CIFAR), 100 \u044d\u043f\u043e\u0445 (FEMNIST), 200 \u044d\u043f\u043e\u0445 (ImageNette).', target: 'Table 1. Top-1 accuracy (%), n=32, 300 epochs (CIFAR), 100 epochs (FEMNIST), 200 epochs (ImageNette).', corrected: 'Table 5. Top-1 accuracy (%), n=32, 300 epochs (CIFAR), 100 epochs (FEMNIST), 200 epochs (ImageNette).' }, { id: 13, section: '\u00a76.5', source: 'ATC-DL (async) \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0435\u0442 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0432 2.4\u00d7 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 D-PSGD \u0438 \u0432 1.4\u00d7 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 ADSGD (\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0439 \u043a\u043e\u043d\u043a\u0443\u0440\u0435\u043d\u0442).', target: 'ATC-DL (async) reaches target accuracy 2.4\u00d7 faster than D-PSGD and 1.4\u00d7 faster than ADSGD (the strongest competitor).', corrected: 'ATC-DL (async) reaches target accuracy 2.4\u00d7 faster than D-PSGD and 1.4\u00d7 faster than ADSGD (the closest competitor).' }, { id: 14, section: '\u00a76.7', source: 'DissDL \u0443\u0445\u0443\u0434\u0448\u0430\u0435\u0442\u0441\u044f \u0441 1.3\u00d7 \u0434\u043e 1.5\u00d7.', target: 'DissDL DEGRADES from 1.3\u00d7 to 1.5\u00d7.', corrected: 'DissDL improves from 1.3\u00d7 to 1.5\u00d7.' }, { id: 15, section: '\u00a77.2', source: 'ATC-DL \u0447\u0430\u0441\u0442\u0438\u0447\u043d\u043e \u0441\u043d\u0438\u043c\u0430\u0435\u0442 \u044d\u0442\u0443 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0443: \u0430\u0434\u0430\u043f\u0442\u0438\u0432\u043d\u0430\u044f \u0442\u043e\u043f\u043e\u043b\u043e\u0433\u0438\u044f \u043e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043d\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u0435 \u0440\u0430\u0441\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439.', target: 'ATC-DL partially addresses this issue: the adaptive topology is guided by actual model divergence.', corrected: 'ATC-DL partially mitigates this issue: the adaptive topology is guided by actual model divergence.' }, { id: 16, section: '\u00a77.5', source: '\u0412\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 Byzantine-\u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u044b\u0445 \u0430\u0433\u0440\u0435\u0433\u0430\u0442\u043e\u0440\u043e\u0432 \u043f\u0440\u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0438\u0438.', target: 'Integration of Byzantine-robust aggregators with coordinated adaptation.', corrected: 'Inclusion of Byzantine-robust aggregators under coordinated adaptation.' }, ], errors: [ { segment_id: 7, category: 'Design / Markup', subcategory: 'Formatting', severity: 'critical', source_span: '[\u043d\u0435\u0442 \u043f\u043e\u0434\u043f\u0438\u0441\u0438 \u0432 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0435]', target_span: 'Table 1: Comparison with related methods', description: 'Translator added a table caption that does not exist in the source; this creates a numbering conflict (\u201cTable 1\u201d) with the Table 1 in \u00a76.4 and breaks all in-text references.', suggestion: 'Drop the inserted caption or renumber subsequent tables.' }, { segment_id: 14, category: 'Accuracy', subcategory: 'Mistranslation', severity: 'major', source_span: '\u0443\u0445\u0443\u0434\u0448\u0430\u0435\u0442\u0441\u044f \u0441 1.3\u00d7 \u0434\u043e 1.5\u00d7', target_span: 'DEGRADES from 1.3\u00d7 to 1.5\u00d7', description: '1.5\u00d7 speedup is larger than 1.3\u00d7, so \u201cdegrades\u201d is internally contradictory. The Russian uses \u201c\u0443\u0445\u0443\u0434\u0448\u0430\u0435\u0442\u0441\u044f\u201d in an ironic register that the translator mis-reads.', suggestion: 'improves from 1.3\u00d7 to 1.5\u00d7' }, { segment_id: 3, category: 'Accuracy', subcategory: 'Addition', severity: 'major', source_span: '\u0441\u0432\u0435\u0440\u0445\u0430\u0434\u0434\u0438\u0442\u0438\u0432\u043d\u044b\u0439 \u0432\u044b\u0438\u0433\u0440\u044b\u0448 \u0432 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438', target_span: 'super-additive gain in convergence speed without degradation of accuracy', description: 'The clause \u201cwithout degradation of accuracy\u201d is not in the source and changes the abstract\'s claim.', suggestion: 'super-additive gain in convergence speed' }, { segment_id: 4, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0439 \u043f\u0430\u0440\u0430\u0434\u0438\u0433\u043c\u043e\u0439', target_span: 'has emerged as a foundational paradigm', description: 'Double shift: tense (present \u2192 present-perfect) and intensifier (\u201ckey\u201d \u2192 \u201cfoundational\u201d). Both are over-statements.', suggestion: 'is a key paradigm' }, { segment_id: 5, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u0442\u0435\u0440\u044f\u044f \u0434\u043e 60% wall-clock \u0432\u0440\u0435\u043c\u0435\u043d\u0438', target_span: 'wasting up to 60% of wall-clock time', description: '\u201cWasting\u201d carries a moral overtone absent from the neutral source verb \u201c\u0442\u0435\u0440\u044f\u0442\u044c\u201d (to lose). \u201cLosing\u201d is more accurate.', suggestion: 'losing up to 60% of wall-clock time' }, { segment_id: 6, category: 'Terminology', subcategory: 'Wrong term', severity: 'minor', source_span: '\u0423\u0447\u0451\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u043e\u0439 \u0433\u0435\u0442\u0435\u0440.', target_span: 'System Heterogeneity', description: 'The table column header drops \u201c\u0443\u0447\u0451\u0442\u201d (awareness/consideration). The other columns in the table also use the \u201cawareness\u201d framing.', suggestion: 'System Heterogeneity Awareness' }, { segment_id: 8, category: 'Accuracy', subcategory: 'Addition', severity: 'minor', source_span: '\u0441\u0442\u043e\u0445\u0430\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0448\u0443\u043c', target_span: 'stochastic gradient noise', description: 'Added \u201cgradient\u201d \u2014 defensible from context but formally an addition.', suggestion: 'stochastic noise' }, { segment_id: 9, category: 'Style', subcategory: 'Awkward', severity: 'minor', source_span: '\u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 grid search', target_span: 'selected via grid search', description: 'Omitted \u201c\u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435\u201d (in practice).', suggestion: 'in practice, selected via grid search' }, { segment_id: 10, category: 'Terminology', subcategory: 'Wrong term', severity: 'minor', source_span: '\u043a\u0430\u043b\u0438\u0431\u0440\u043e\u0432\u0430\u043d\u044b', target_span: 'fitted', description: '\u201cCalibration\u201d and \u201cfitting\u201d are distinct statistical procedures.', suggestion: 'calibrated against' }, { segment_id: 11, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u0414\u043b\u044f \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0441\u0442\u0438', target_span: 'For correctness', description: 'Literal rendering; idiomatic English research usage is \u201cfor fairness\u201d.', suggestion: 'For fairness' }, { segment_id: 12, category: 'Design / Markup', subcategory: 'Formatting', severity: 'minor', source_span: '\u0422\u0430\u0431\u043b\u0438\u0446\u0430 1.', target_span: 'Table 1.', description: 'Conflicts with the table inserted in \u00a71.2 (see seg #7). Should be Table 5 after renumbering.', suggestion: 'Table 5.' }, { segment_id: 13, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0439 \u043a\u043e\u043d\u043a\u0443\u0440\u0435\u043d\u0442', target_span: 'the strongest competitor', description: '\u201c\u0411\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u0439\u201d means closest-in-performance, not strongest.', suggestion: 'the closest competitor' }, { segment_id: 15, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u0447\u0430\u0441\u0442\u0438\u0447\u043d\u043e \u0441\u043d\u0438\u043c\u0430\u0435\u0442', target_span: 'partially addresses', description: '\u201c\u0421\u043d\u0438\u043c\u0430\u0435\u0442\u201d is stronger than \u201caddresses\u201d; \u201cmitigates\u201d matches.', suggestion: 'partially mitigates' }, { segment_id: 16, category: 'Style', subcategory: 'Unidiomatic', severity: 'minor', source_span: '\u0412\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435', target_span: 'Integration', description: '\u201c\u0412\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\u201d = inclusion. \u201cIntegration\u201d implies deeper interleaving than the source.', suggestion: 'Inclusion \u2026 under' }, ], corrected_full_text: '(Demo project \u2014 full corrected text omitted; use Download .docx after a real run.)', }, }; // Compute metrics + lev distance for the demo so the Results screen renders correctly. (function compute() { const data = window.__APE_DEMO_PROJECT.data; const counts = { critical: 0, major: 0, minor: 0 }; let penalty = 0; for (const e of data.errors) { const sev = (e.severity || 'minor').toLowerCase(); counts[sev]++; penalty += window.SEVERITY_WEIGHTS[sev]; } const dimCounts = {}; window.MQM_DIMENSIONS.forEach((d) => { dimCounts[d.label] = 0; }); for (const e of data.errors) { if (dimCounts[e.category] !== undefined) dimCounts[e.category]++; } const wc = data.source_word_count; data.metrics = { counts, penalty, quality_score: Math.max(0, (1 - penalty / wc) * 100), source_word_count: wc, dimension_counts: dimCounts, }; for (const seg of data.segments) { const a = seg.target || '', b = seg.corrected || ''; if (a && b && a !== b) { seg.lev_distance = window.levenshtein(a, b); seg.lev_norm = seg.lev_distance / Math.max(a.length, b.length); } else { seg.lev_distance = 0; seg.lev_norm = 0; } } })();