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Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer

by   Bhaskar Sen, et al.

Quality of questions and answers from community support websites (e.g. Microsoft Developers Network, Stackoverflow, Github, etc.) is difficult to define and a prediction model of quality questions and answers is even more challenging to implement. Previous works have addressed the question quality models and answer quality models separately using meta-features like number of up-votes, trustworthiness of the person posting the questions or answers, titles of the post, and context naive natural language processing features. However, there is a lack of an integrated question-answer quality model for community question answering websites in the literature. In this brief paper, we tackle the quality Q A modeling problems from the community support websites using a recently developed deep learning model using bidirectional transformers. We investigate the applicability of transfer learning on Q A quality modeling using Bidirectional Encoder Representations from Transformers (BERT) trained on a separate tasks originally using Wikipedia. It is found that a further pre-training of BERT model along with finetuning on the Q As extracted from Microsoft Developer Network (MSDN) can boost the performance of automated quality prediction to more than 80 are carried out for deploying the finetuned model in real-time scenario using AzureML in Azure knowledge base system.


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