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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Deep Learning for Natural Language Processing Methods and Applications

ISBN

978-954-452-092-2

ISSN

—

e-ISSN

2603-2813

Počet stran výsledku

12

Strana od-do

824-835

Název nakladatele

INCOMA Ltd.

Místo vydání

Shoumen

Místo konání akce

Varna

Datum konání akce

4. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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