Bibliographic record
Abstract
In this paper, we applied deep learning framework to tackle the tasks of finding duplicate questions. We implemented some models following the siamese architecture using the popular recurrent network such as Long-Short term memory (LSTM), Bi-direction Long-Short term memory (biLSTM) to find the semantic similarity between questions. We started with a basic model and further extended the basic model into three different models. Our models provide a refined, composite representation of the questions. The addition of Convolutional Neural Network (CNN) with the recurrent networks is a new approach for the sentence representation. We also applied attention mechanism for getting better contextual meaning of the questions. We generated a representation of a question according to the context of another question for solving the task. As neural models are data driven, we trained our models extensively by making pairs, such as question-question over a large-scale real-life dataset. We used a datset consisting of 400K labeled question pairs which are published by a well known question-answer forum Quora. We evaluate our models based on metrics like accuracy, precision, recall, F1 scores. Our methods and experiments demonstrate some significant improvements over the baseline systems and the state-of-the-art systems.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".