Data · dataset · 2026
A dataset of Tang poetry sentence pairs for metrical imitation
Listed in ScienceDB
Large Language Models (LLMs) often exhibit deficiencies in tonal patterns (pingze), structural parallelism, and imagery diversity when performing stylistic imitation of Tang poetry.
Description
To address these challenges, this study presents a specialized paired corpus of Tang poetry imitation used for metrical imitation tasks. Sourced from The Complete Tang Poems (Quan Tangshi), the dataset comprises 17,960 entries across four distinct genres: pentasyllabic/heptasyllabic quatrains, pentasyllabic regulated verses, heptasyllabic quatrains, and heptasyllabic regulated verses.
Each entry consists of an original poetic couplet line pair and a corresponding manually crafted imitation line pair. All imitation line pairs were created by specialists in Classical Chinese. While strictly following the metrical patterns of the original lines, the generated imitations underwent a triple-verification process—covering metrical conformity, antithetical parallelism, and imagery consistency, ensuring that no core content words were repeated from the original lines.
Read the rest (1 more)
A dual-person review of a randomly selected 5% sample showed that 94.9% of the samples passed all three evaluation criteria: metrical conformity, antithetical parallelism, and imagery consistency. For the Qwen3-8B, metrical accuracy increased from 32.8% to 62.8%, the pass rate improved from 66.8% to 86.2%, and the imagery diversity score improved from 0.48 to 0.76. This dataset can be used for supervised training of regulated poetry generation models and offers data resources for text generation tasks involving strong formal constraints.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.32366 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Linguistics 72% · Text 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.32366 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4704 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (72%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:text | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |