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Record W2903963674 · doi:10.1609/aaai.v33i01.33017459

Generating Chinese Ci with Designated Metrical Structure

2019· article· en· W2903963674 on OpenAlexaff
Richong Zhang, Xinyu Liu, Xinwei Chen, Zhiyuan Hu, Zhaoqing Xu, Yongyi Mao

Bibliographic record

VenueProceedings of the AAAI Conference on Artificial Intelligence · 2019
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaBeijing Advanced Innovation Center for Big Data and Brain ComputingState Key Laboratory of Software Development EnvironmentNational Natural Science Foundation of China
KeywordsSubject (documents)Computer sciencePoetryArtificial intelligenceNatural language processingLinguisticsPhilosophyWorld Wide Web

Abstract

fetched live from OpenAlex

Ci is a lyric poetry form that follows highly restrictive metrical structures. This makes it challenging for a computer to compose Ci subject to a specified metrical requirement. In this work, we adapt the CVAE framework to automated Ci generation under metrical constraints. Specifically, we present the first neural model that explicitly encodes the designated metrical structure for Ci generation. The proposed model is shown experimentally to generate Ci with nearly perfect metrical structures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.269
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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