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Register a new userMe, myself, and ire: Effects of automatic transcription quality on emotion, sarcasm, and personality detection
لي، أنا، وأنا IRE: آثار جودة النسخ التلقائي على العاطفة والسخرية والكشف عن الشخصية
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في النشر، يجب أن تستخدم النظم التي تستخدم الكلام كمدخلات من النسخ الآلي.ومع ذلك، عادة عندما يتم تقييم هذه الأنظمة، يفترض أن نسخ الذهب.نحن ندرس صراحة تأثير أخطاء النسخ على الأداء المصاب لنظام متعدد الوسائط على ثلاثة مهام ذات صلة من ثلاث مجموعات بيانات: المشاعر والتهكية والكشف عن الشخصية.نضم ثلاثة أدوات نسخ منفصلة وإظهار أنه في حين أن جميع عمليات النسخ الآلية تنتشر أخطاء تؤثر بشكل كبير على أداء المصب، فإن أدوات المصدر المفتوح هي أسوأ من الأداة المدفوعة، على الرغم من أنها ليست دائما بشكل مباشر، ومعدلات خطأ Word لا ترتبط بشكل جيد مع أداء المصب.نجد كذلك أن إدراج ميزات الصوت يخفف جزئيا أخطاء النسخ، ولكن أن الاستخدام السذاجة لإعداد متعددة المهام لا.
In deployment, systems that use speech as input must make use of automated transcriptions. Yet, typically when these systems are evaluated, gold transcriptions are assumed. We explicitly examine the impact of transcription errors on the downstream performance of a multi-modal system on three related tasks from three datasets: emotion, sarcasm, and personality detection. We include three separate transcription tools and show that while all automated transcriptions propagate errors that substantially impact downstream performance, the open-source tools fair worse than the paid tool, though not always straightforwardly, and word error rates do not correlate well with downstream performance. We further find that the inclusion of audio features partially mitigates transcription errors, but that a naive usage of a multi-task setup does not.
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We present three methods developed for the Shared Task on Sarcasm and Sentiment Detection in Arabic. We present a baseline that uses character n-gram features. We also propose two more sophisticated methods: a recurrent neural network with a word lev
el representation and an ensemble classifier relying on word and character-level features. We chose to present results from an ensemble classifier but it was not very successful as compared to the best systems : 22th/37 on sarcasm detection and 15th/22 on sentiment detection. It finally appeared that our baseline could have been improved and beat those results.
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Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in
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Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion. In contrast to theories of basic emotions or affect (valence/arousal), this theory has not received a lot of attention in natural language processing.
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The introduction of transformer-based language models has been a revolutionary step for natural language processing (NLP) research. These models, such as BERT, GPT and ELECTRA, led to state-of-the-art performance in many NLP tasks. Most of these mode
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