Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
Bibliographic record
Abstract
It is generally agreed upon that creativity is an important property of human language. For example, speakers routinely coin new words, employ novel metaphors, and play with words through puns. Indeed, such creative processes take place at all levels of language from the lexicon, to syntax, semantics, and discourse. Creativity allows speakers to express themselves with their own individual style. It further provides new ways of looking at the world, by describing something through the use of unusual comparisons for effect or emphasis, and thus making language more engaging and fun. Listeners are typically able to understand creative language without any difficulties. On the other hand, generating and recognizing creative language presents a tremendous challenge for natural language processing (NLP) systems. The recognition of instances of linguistic creativity, and the computation of their meaning, constitute one of the most challenging problems for a variety of NLP tasks, such as machine translation, text summarization, information retrieval, dialog systems, and sentiment analysis. Moreover, models of linguistic creativity are necessary for systems capable of generating story narratives, jokes, or poetry. Nevertheless, despite the importance of linguistic creativity in many NLP tasks, it still remains unclear how to model, simulate, or evaluate linguistic creativity. Furthermore, research on topics related to linguistic creativity has not received a great deal of attention at major computational linguistics conferences in recent years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.071 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".