Publications

@conference{shirani2019question,

title = {Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models},

author = {Amirreza Shirani, Bowen Xu, David Lo, Thamar Solorio, Amin Alipour},

url = {https://arxiv.org/pdf/1905.01966.pdf},

year  = {2019},

date = {2019-01-03},

booktitle = {AAAI Reasoning for Complex Question Answering

Workshop (AAAI 2019)},

abstract = {Domain-specific community question answering is becoming an integral part of professions. Finding related questions

and answers in these communities can significantly improve

the effectiveness and efficiency of information seeking. Stack

Overflow is one of the most popular communities that is being

used by millions of programmers. In this paper, we analyze

the problem of predicting knowledge unit (question thread)

relatedness in Stack Overflow. In particular, we formulate the

question relatedness task as a multi-class classification problem with four degrees of relatedness.

We present a large-scale dataset with more than 300K pairs.

To the best of our knowledge, this dataset is the largest

domain-specific dataset for Question-Question relatedness.

We present the steps that we took to collect, clean, process,

and assure the quality of the dataset. The proposed dataset

on Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the

prediction of relatedness in technical community question-answering forums.

We adapt a neural network architecture and a traditional

model for this task that effectively utilize information from

different parts of knowledge units to compute the relatedness

between them. These models can be used to benchmark novel

models, as they perform well in our task and in a closely similar task.},

keywords = {Community question answering, cQA, Question relatedness, stack overflow},

pubstate = {published},

tppubtype = {conference}

}