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Register a new userBLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-Oriented Text
بلو، نيزك، Bertscore: تقييم أداء المقاييس في تقييم أخطاء الترجمة الهامة في النص الموجه نحو المعنويات
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شركات وسائل التواصل الاجتماعي وكذلك سلطات الرقابة تجعل الاستخدام المكثف للأدوات الذكاء الاصطناعي (AI) لمراقبة منشورات خطاب الكراهية أو الاحتفالات بالعنف أو الألفاظ النابية. نظرا لأن برنامج AI يتطلب كميات كبيرة من البيانات لتدريب أجهزة الكمبيوتر، يتم تنفيذ الترجمة التلقائية للمحتوى عبر الإنترنت عادة للتعويض عن ندرة النص في بعض اللغات. ومع ذلك، فإن أخطاء الترجمة الآلية (MT) هي حدوث منتظم عند ترجمة المحتوى الذي تم إنشاؤه الموجه نحو المستخدم (UGC)، خاصة عند مشاركة لغة الموارد المنخفضة. في مثل هذه السيناريوهات، تعتمد كفاية العملية برمتها على افتراض أن الترجمة يمكن تقييمها بشكل صحيح. في هذه الورقة، نقوم بتقييم قدرة مقاييس الجودة التلقائية على اكتشاف أخطاء ترجمة الآلات الحرجة التي يمكن أن تسبب سوء فهم خطير في رسالة التأثير. نقارن أداء ثلاثة مقاييس قانوني على الترجمات التي لا معنى لها بالمقارنة مع الترجمات ذات مغزى مع وجود خطأ حاسم يشوه المعنويات الإجمالية للنص المصدر. نوضح الحاجة إلى ضبط القيود التلقائي لجعلها أكثر قوة في اكتشاف أخطاء المعنويات الحرجة.
Social media companies as well as censorship authorities make extensive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train computers, automatic-translation of the online content is usually implemented to compensate for the scarcity of text in some languages. However, machine translation (MT) mistakes are a regular occurrence when translating sentiment-oriented user-generated content (UGC), especially when a low-resource language is involved. In such scenarios, the adequacy of the whole process relies on the assumption that the translation can be evaluated correctly. In this paper, we assess the ability of automatic quality metrics to detect critical machine translation errors which can cause serious misunderstanding of the affect message. We compare the performance of three canonical metrics on meaningless translations as compared to meaningful translations with a critical error that distorts the overall sentiment of the source text. We demonstrate the need for the fine-tuning of automatic metrics to make them more robust in detecting sentiment critical errors.
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The location of wireless sensor nodes located in the center is necessary for applications
where information about the site is important information such as security, protection,
object tracking and other applications.
localization algorithms are c
lassified into two types: Range-based and Range-free. The
study focused on Range-free localization algorithms because they are less expensive in
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The MATLAB was used to simulate the algorithms, whose performance was evaluated by
changing the number of network nodes, the number of Anchor nodes, and the contract area
of communication in order to illustrate performance differences in terms of localization
error.
The results showed the superiority of the amorphous algorithm, achieving high localization
accuracy and lower cost for the number of Anchor nodes needed to achieve a small error.
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