دوره 21، شماره 1 - ( 3-1403 )                   جلد 21 شماره 1 صفحات 142-125 | برگشت به فهرست نسخه ها


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Nourollahi S F, Baradaran R, Amirkhani H. Domain adaptation-based method for improving generalization of hate speech detection models. JSDP 2024; 21 (1) : 10
URL: http://jsdp.rcisp.ac.ir/article-1-1341-fa.html
نوراللهی سیده فاطمه، برادران راضیه، امیرخانی حسین. بهبود قدرت تعمیم مدل‌های تشخیص کلام نفرت‌انگیز مبتنی بر تطبیق دامنه. پردازش علائم و داده‌ها. 1403; 21 (1) :125-142

URL: http://jsdp.rcisp.ac.ir/article-1-1341-fa.html


دانشگاه قم
چکیده:   (238 مشاهده)
امروزه با رشد فعالیت در شبکه‌های اجتماعی شاهد افزایش کلام نفرت‌انگیز به صورت برخط هستیم و به‌همین منظور مسئلۀ تشخیص نفرت در فضای مجازی دارای اهمیت است. همچنین تطبیق دامنه نیز در این مسئله و به‌طورکلی در حوزۀ پردازش زبان طبیعی، یکی از چالش‌های مهم است. در بسیاری از مسائل، ضمن تغییر دامنه با افت عملکرد مواجهیم که این موضوع در مسئلۀ نفرت نیز صادق است. در این پژوهش با استفاده از روش‌های تطبیق دامنه سعی در افزایش قدرت تعمیم‌پذیری مدل‌های تشخیص نفرت خواهیم داشت. برای این منظور روش‌های مبتنی بر ترنسفورمر شامل آموزش خصمانۀ دامنه و ترکیب متخصصان را به کار می‌گیریم و همچنین از آموزش چند منبعی استفاده می‌کنیم. آزمایش‌ها با استفاده از چهار مجموعه‌داده در حوزۀ نفرت انجام می‌شوند. در ابتدا مد‌ل‌ها را به‌صورت درون‌ دامنه‌ای و تک منبعی ارزیابی می‌کنیم. در مرحلۀ بعد با اضافه کردن دامنه‌های دیگر به بخش آموزش، شاهد افت نتایج و انتقال منفی هستیم. سپس آزمایش‌های برون دامنه‌ای را ابتدا به‌صورت تک منبعی با مدل DistilBERT انجام می‌دهیم که با تغییر دامنه نتایج به طور قابل توجهی کاهش می‌یابند. به‌منظور افزایش قدرت تطبیق دامنۀ مدل‌ در بخش برون دامنه‌ای، روی چند منبع آموزش را انجام می‌دهیم که حدوداً در نیمی از موارد سبب بهبود نتایج می‌شود که نتیجۀ معناداری نیست. در ادامه با استفاده از روش‌های مبتنی بر ترنسفورمر شامل آموزش خصمانۀ دامنه و ترکیب متخصصان سعی در افزایش قدرت تطبیق دامنۀ مدل‌ها خواهیم داشت که در 87% از آزمایش‌های برون دامنه‌ای چند منبعی شاهد افزایش عملکرد هستیم. البته این روش‌ها در عملکرد آزمایش‌های درون دامنه‌ای هم مؤثر هستند. مسئلۀ مهمی که گاهی موجب افت‌وخیز چشمگیر نتایج می‌شود، مجموعه‌داده‌ها هستند. شباهت داده‌ها و تشابه توزیع بعضی دامنه‌ها باعث افزایش قدرت تطبیق دامنۀ مدل می‌شوند.
 
شماره‌ی مقاله: 10
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نوع مطالعه: كاربردي | موضوع مقاله: مقالات پردازش متن
دریافت: 1401/7/2 | پذیرش: 1402/12/6 | انتشار: 1403/5/13 | انتشار الکترونیک: 1403/5/13

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