PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering
Bibliographic record
Abstract
Recent years have witnessed a high interest in non-factoid question answering using Community Question Answering (CQA) web sites.Despite ongoing research using state-of-the-art methods, there is a scarcity of available datasets for this task.Why-questions, which play an important role in open-domain and domain-specific applications, are difficult to answer automatically since the answers need to be constructed based on different information extracted from multiple knowledge sources.We introduce the PhotoshopQuiA dataset, a new publicly available set of 2,854 why-question and answer(s) (WhyQ, A) pairs related to Adobe Photoshop usage collected from five CQA web sites.We chose Adobe Photoshop because it is a popular and well-known product, with a lively, knowledgeable and sizable community.To the best of our knowledge, this is the first English dataset for Why-QA that focuses on a product, as opposed to previous open-domain datasets.The corpus is stored in JSON format and contains detailed data about questions and questioners as well as answers and answerers.The dataset can be used to build Why-QA systems, to evaluate current approaches for answering why-questions, and to develop new models for future QA systems research.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".