By the community & for the community: a deep learning approach to assist collaborative editing in Q&A sites

Chunyang Chen, Zhenchang Xing, Yang Liu

Research output: Contribution to journalArticleResearchpeer-review

22 Citations (Scopus)

Abstract

Community edits to questions and answers (called post edits) plays an important role in improving content quality in Stack Overflow. Our study of post edits in Stack Overflow shows that a large number of edits are about formatting, grammar and spelling. These post edits usually involve small-scale sentence edits and our survey of trusted contributors suggests that most of them care much or very much about such small sentence edits. To assist users in making small sentence edits, we develop an edit-assistance tool for identifying minor textual issues in posts and recommending sentence edits for correction. We formulate the sentence editing task as a machine translation problem, in which an original sentence is "translated" into an edited sentence. Our tool implements a character-level Recurrent Neural Network (RNN) encoder-decoder model, trained with about 6.8 millions original-edited sentence pairs from Stack Overflow post edits. We evaluate our edit assistance tool using a large-scale archival post edits, a field study of assisting a novice post editor, and a survey of trusted contributors. Our evaluation demonstrates the feasibility of training a deep learning model with post edits by the community and then using the trained model to assist post editing for the community.

Original languageEnglish
Article number32
Number of pages21
JournalProceedings of the ACM on Human-Computer Interaction
Volume1
Issue numberCSCW
DOIs
Publication statusPublished - 1 Nov 2017
Externally publishedYes

Keywords

  • Collaborative editing
  • Deep learning
  • Q&A sites

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