Predicting Tags for Learner Questions on Stack Overflow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Online question answering sites, such as Stack Overflow (SO), have become an important learning and support platform for computer-science learners and practitioners who are seeking help. Learners on SO are currently faced with the problem of unanswered questions, inhibiting their lifelong-learning efforts and contributing to delays in their software development process. The major reason for this problem is that most of the technical problems posted on SO are not seen by those who have the required expertise and knowledge to answer a specific question. This issue is often attributed to the use of inappropriate tags when posting questions. We developed a new method, BERT-CBA, to predict tags for answering user questions. BERT-CBA combines a convolutional network, BILSTM, and attention layers with BERT. In BERT-CBA, the convolutional layer extracts the local semantic features of an SO post, the BILSTM layer fuses the local semantic features and the word embeddings (contextual features) of an SO post, and the attention layer selects the important words from a post to identify the most appropriate tag labels. BERT-CBA outperformed four existing tag recommendation approaches by 2-73% as measured by F1@K=1-5. These findings suggest that BERT-CBA could be used to recommend appropriate tags to learners before they post their question which would increase their chances of getting answers.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it