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MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970053" target="_blank" >RIV/49777513:23520/23:43970053 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2023.ranlp-1.89/" target="_blank" >https://aclanthology.org/2023.ranlp-1.89/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.26615/978-954-452-092-2_089" target="_blank" >10.26615/978-954-452-092-2_089</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain

  • Original language description

    This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, fine-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overflow search system, where it can help users find answers for already answered questions. Alongside the MQDD model, we release two datasets related to the software engineering domain. The first Stack Overflow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overflow Duplicity Dataset (SODD) contains data for training duplicate detection models.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Deep Learning for Natural Language Processing Methods and Applications

  • ISBN

    978-954-452-092-2

  • ISSN

  • e-ISSN

    2603-2813

  • Number of pages

    12

  • Pages from-to

    824-835

  • Publisher name

    INCOMA Ltd.

  • Place of publication

    Shoumen

  • Event location

    Varna

  • Event date

    Sep 4, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article