PTM4Tag: sharpening tag recommendation of stack overflow posts with pre-trained models

PTM4Tag: sharpening tag recommendation of stack overflow posts with pre-trained models

Stack Overflow is often viewed as one of the most influential Software Question & Answer (SQA) websites, containing millions of programming-related questions and answers. Tags play a critical role in efficiently structuring the contents in Stack Overflow and are vital to support a range of site...

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Bibliographic Details
Main Authors: HE, Junda, XU, Bowen, YANG, Zhou, HAN, DongGyun, YANG, Chengran, LO, David
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Tag Recommendation
Transformer
Pre-Trained Models
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/7689
https://ink.library.smu.edu.sg/context/sis_research/article/8692/viewcontent/PTM.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Stack Overflow is often viewed as one of the most influential Software Question & Answer (SQA) websites, containing millions of programming-related questions and answers. Tags play a critical role in efficiently structuring the contents in Stack Overflow and are vital to support a range of site operations, e.g., querying relevant contents. Poorly selected tags often introduce extra noise and redundancy, which raises problems like tag synonym and tag explosion. Thus, an automated tag recommendation technique that can accurately recommend high-quality tags is desired to alleviate the problems mentioned above.

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