@inproceedings{xu-etal-2018-preferred,
title = "Preferred Answer Selection in {S}tack {O}verflow: Better Text Representations ... and Metadata, Metadata, Metadata",
author = "Xu, Steven and
Bennett, Andrew and
Hoogeveen, Doris and
Lau, Jey Han and
Baldwin, Timothy",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6119",
doi = "10.18653/v1/W18-6119",
pages = "137--147",
abstract = "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 attention-based model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary meta-data, achieves a substantially higher result over two reference datasets novel to this work.",
}
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<abstract>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 attention-based model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary meta-data, achieves a substantially higher result over two reference datasets novel to this work.</abstract>
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%0 Conference Proceedings
%T Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata
%A Xu, Steven
%A Bennett, Andrew
%A Hoogeveen, Doris
%A Lau, Jey Han
%A Baldwin, Timothy
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-preferred
%X 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 attention-based model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary meta-data, achieves a substantially higher result over two reference datasets novel to this work.
%R 10.18653/v1/W18-6119
%U https://aclanthology.org/W18-6119
%U https://doi.org/10.18653/v1/W18-6119
%P 137-147
Markdown (Informal)
[Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata](https://aclanthology.org/W18-6119) (Xu et al., WNUT 2018)
ACL