Human-Translated Estonian Dataset

• Human Translated Estonian Dataset is a curated language resource where expert translators ensure cultural adaptation, task constraint preservation, and linguistic fidelity.
• The dataset incorporates detailed morphosyntactic annotations, TEI-encoded XML formats, and structured alignment to support robust evaluation of multilingual models.
• Empirical evaluations reveal that human-translated corpora maintain higher accuracy and semantic integrity compared to machine-translated datasets in commonsense reasoning tasks.

A human translated Estonian dataset refers to a language resource in which source texts—typically from high-resource languages such as English—are rendered into standard Estonian by expert human translators, often supported by rigorous quality control and domain-specific adaptation. Such datasets play a critical role in both natural language understanding (NLU) research involving Estonian and in the development and benchmarking of multilingual models. Key manifestations include parallel corpora for translation, morphosyntactic annotation resources, and localized evaluation benchmarks for tasks such as commonsense reasoning.

1. Methodology of Human Translation and Adaptation

The creation of a human translated Estonian dataset is characterized by multilayered processes emphasizing fidelity, naturalness, and task-specific constraints. In benchmarking scenarios, as exemplified by the Estonian translation of the WinoGrande commonsense reasoning dataset, the main steps include:

• Specialized Translation Team: The translation is conducted by advanced translation-studies students and reviewed by professional translators to ensure both linguistic and contextual accuracy (Ojastu et al., 21 Nov 2025).
• Task Constraint Preservation: Original dataset constraints, such as ≥70% lexical overlap in “twin sentences” and case-match for answer options (to address Estonian’s agglutinative morphology), are explicitly maintained. Literal translations are modified where they introduce ambiguity or unintended cues (e.g., “cheap” → “maitsetu” instead of the price-oriented “odav”) (Ojastu et al., 21 Nov 2025).
• Cultural and Linguistic Localization: References to locations, foods, brands, and proper names are systematically adapted to Estonian equivalents, inflected for gender and case to maintain naturalistic language and to avoid cultural mismatches (Ojastu et al., 21 Nov 2025).
• Quality Assurance: Multi-round annotation incorporates independent relabeling by additional expert annotators, with stringent agreement metrics (Cohen’s κ = 0.816, Fleiss’ κ = 0.855), and the correction of mislabelled or ambiguous items in the original English source. The index of correct answer options is preserved between languages (Ojastu et al., 21 Nov 2025).

2. Dataset Structure and Linguistic Complexity

Human translated Estonian datasets are characterized by detailed structural and linguistic annotation to reflect the morphosyntactic properties of Estonian.

• Scale and Coverage: The Estonian WinoGrande test set comprises 1,767 examples, with a distinct subset localized or corrected; others retain comparability to the English source. Comparable efforts in the MULTEXT-East Estonian subcorpus offer on the order of 100,000 tokens in several thousand sentences, as found in the hand-translated and morphosyntactically annotated "1984" parallel corpus (Ojastu et al., 21 Nov 2025, Erjavec, 2020).
• Morphological Inflection: Estonian requires inflection in up to 14 grammatical cases, impacting both source and answer slot renderings. Morphological cues, including number and gender, are scrutinized to avoid spurious shortcuts for models and to maintain the integrity of reasoning tasks (Ojastu et al., 21 Nov 2025).
• Data Format and Encoding: In linguistically annotated corpora such as MULTEXT-East, tokens are encoded in TEI P5 XML with detailed lemma and morphosyntactic descriptors; alignment files provide sentence-level mapping to source languages (Erjavec, 2020).

3. Evaluation Protocols and Performance Assessment

Comprehensive evaluation protocols are standardized in human translated Estonian datasets to enable meaningful cross-lingual benchmarks.

• Model Coverage: Both proprietary (e.g., Gemini 2.5, Claude Sonnet 4.5, GPT-4.1, GPT-5) and open-source LLMs (e.g., Llama 3, Gemma 3, Qwen 2.5, EuroLLM) are systematically evaluated, often in few-shot (3-shot) paradigms using corresponding Estonian dev examples (Ojastu et al., 21 Nov 2025).
• Primary Metric: Accuracy, defined as the number of correct predictions over the task’s total instances, is the principal evaluation metric. No explicit BLEU or surface translation metrics are reported in commonsense reasoning settings (Ojastu et al., 21 Nov 2025).
• Comparative Frameworks: Results are benchmarked between English originals, human translated Estonian test sets (HT), and two forms of machine-translated datasets (MT Simple/MT Detailed), enabling direct quantification of translation and adaptation effects on downstream model reasoning.

4. Human Translation Versus Machine Translation

Empirical comparison underscores the persistent superiority of human translation in preserving both intended meaning and the task’s structural requirements.

• Accuracy Differential: Across multiple model classes, accuracy on human translated Estonian (HT) is consistently higher than on machine-translated (MT) test sets. For proprietary models, the gap is 6.4–6.7 percentage points relative to MT (Simple/Detailed); for open moderate-large models, 5.2–5.9 percentage points. Models perform only marginally worse on HT (–0.4 percentage point relative to English), but much worse on MT, with particular degradation for smaller models (Ojastu et al., 21 Nov 2025).
• Translation Artifacts: Machine translation introduces artifacts, including case-agreement mismatches, disruptions of lexical overlap, and unintended ambiguity, which are less prevalent or absent in human-translated datasets (Ojastu et al., 21 Nov 2025).
• Prompt Engineering Limitations: Even detailed, few-shot prompt designs aimed at guiding machine translation to address Estonian-specific issues offer only negligible improvements over basic prompts, with meaning-shift rates reduced from 15.2% (MT Simple) to 11.8% (MT Detailed) but no substantive accuracy gains (Ojastu et al., 21 Nov 2025).
• Error Types and Meaning-Shift: Human translation ensures context-appropriate synonym selection, avoidance of linguistic traps, and maintenance of schema intent. Machine translation often broadens or narrows meanings unintentionally, resulting in datasets that can no longer reliably probe commonsense reasoning (Ojastu et al., 21 Nov 2025).

5. Morphosyntactic Estonian Datasets: MULTEXT-East

In parallel with task-oriented benchmarks, resources such as the Estonian component of MULTEXT-East provide foundational infrastructure for Estonian language technology.

• Three-Part Resource Suite: MULTEXT-East Estonian comprises (1) an EAGLES-based morphosyntactic tagset and specification, (2) a mid-sized lexicon with inflected forms, lemmas, and morphosyntactic descriptors, and (3) a hand-validated, fully TEI-encoded corpus (Estonian translation of "1984") aligned at sentence level with 16 other languages, including English (Erjavec, 2020).
• Morphosyntactic Tagging: The Estonian tagset comprises 642 unique morphosyntactic descriptors (MSDs), capturing the complexity of Estonian morphology. Tags are mapped to full feature structures, e.g., $$\phi(\text{Nc-sn}) = \langle\text{Category=Noun},\ \text{Type=common},\ \text{Number=singular},\ \text{Case=nominative}\rangle$$ (Erjavec, 2020).
• Alignment and Encoding: Manually validated sentence alignments allow cross-lingual studies, leveraging XML structures for interoperability. Files are distributed as UTF-8, with validation against custom schemas (Erjavec, 2020).
• Access: The MULTEXT-East Estonian dataset is freely available for research, contingent on registration and acknowledgment of usage (Erjavec, 2020).

6. Implications for Model Evaluation and Benchmark Design

The deployment of human translated Estonian datasets has direct implications for the evaluation of LLMs and the construction of reliable multilingual benchmarks.

• Benchmark Reliability: Expert human translation is essential to prevent morphosyntactic cues, ambiguities, or semantic shifts that would undermine task validity or misrepresent model reasoning abilities (Ojastu et al., 21 Nov 2025).
• Quality Control Best Practices: Rigorous annotation protocols—including cultural adaptation, schema integrity preservation, and systematic inter-annotator agreement reporting—are necessary for high-fidelity evaluation (Ojastu et al., 21 Nov 2025).
• Strategic Recommendations: Future efforts should engage bilingual experts from project inception, maintain alignment with source task constraints, perform multi-stage quality control, and consider the development of native, language-specific resources instead of relying solely on translations (Ojastu et al., 21 Nov 2025).
• Public Release of Artifacts: Datasets should accompany both human and machine translation variants, with explicit labelling of corrected and problematic instances to support transparency in error analysis (Ojastu et al., 21 Nov 2025).

7. Summary Table: Key Human Translated Estonian Resources

Dataset/Resource	Composition/Content	Principal Features
WinoGrande (Estonian)	1,767 test schemas; human translation, cultural adaptation	Lexical overlap, morphological adaptation, QC
MULTEXT-East (Estonian)	100,000-token “1984” parallel corpus, lexicon, MSD spec	TEI P5 XML, EAGLES-based tagging, sentence align.

These resources collectively provide high-quality infrastructure for both the advancement of Estonian NLP and the robust evaluation of multilingual models (Ojastu et al., 21 Nov 2025, Erjavec, 2020).