Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata
Steven X. Xu, Andrew R. Bennett, Doris Hoogeveen, Jey Han Lau, Timothy J. Baldwin · 2018
Community question answering (cQA) forums provide a rich source of data for facilitating non-factoid question answering over many technical domains.Given this, there is considerable interest in answer retrieval from these kinds of forums.However this is a difficult task as the structure of these forums is very rich, and both metadata and text features are important for successful retrieval.While there has recently been a lot of work on solving this problem using deep learning models applied to question/answer text, this work has not looked at how to make use of the rich metadata available in cQA forums.We propose an attentionbased model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary metadata, achieves a substantially higher result over two reference datasets novel to this work.