Sample code to extract different types of users in CQA in particular context
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Updated
May 10, 2017 - Python
Sample code to extract different types of users in CQA in particular context
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.
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״
Vega-Lite Chart Dataset and NL Generation Framework using LLMs
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