Predicting Future Collaborations in Community-based Question Answering (CQA) Networks
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Updated
May 6, 2017 - R
Predicting Future Collaborations in Community-based Question Answering (CQA) Networks
Sample code to extract different types of users in CQA in particular context
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
SoftSVM implementation
Implementation of "Distilling Knowledge for Fast Retrieval-based Chat-bots" (SIGIR 2020) using deep matching transformer networks and knowledge distillation for response retrieval in information-seeking conversational systems.
A code quality assurance tool for Unity 3D.
NS-CQA: the model of the JWS paper 'Less is More: Data-Efficient Complex Question Answering over Knowledge Bases.' This work has been accepted by JWS 2020.
Official PyTorch implementation for ״ lassification-Regression for Chart Comprehension״
Code Samples Validation (v2 Dashboard) - automated testing with centralized reporting based on GitHub Actions, Playwright and Docusaurus
Vega-Lite Chart Dataset and NL Generation Framework using LLMs
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