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Register a new userThe Importance of Modeling Social Factors of Language: Theory and Practice
أهمية النمذجة العوامل الاجتماعية للغة: النظرية والممارسة
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تطبيقات معالجة اللغة الطبيعية (NLP) أصبحت الآن أكثر قوة في كل مكان من أي وقت مضى. مع النماذج السريعة النامية (العصبية) والبيانات الأكثر إتاحة دائما، فإن نماذج NLP الحالية لديها حق الوصول إلى مزيد من المعلومات من أي مكبر صوت بشري خلال حياتهم. ومع ذلك، سيكون من الصعب القول بأن نماذج NLP وصلت إلى سعة على مستوى بشري. في هذه الورقة الموضعية، نجادل بأن سبب القيود الحالية هو التركيز على محتوى المعلومات مع تجاهل العوامل الاجتماعية للغة. نظهر أن أنظمة NLP الحالية تنهار بشكل منهجي عند مواجهة تفسير العوامل الاجتماعية للغة. يؤدي هذا إلى حدود الطلبات الفرعية من المهام المتعلقة بالمعلومات ويمنع NLP من الوصول إلى أداء المستوى البشري. في الوقت نفسه، تظهر الأنظمة التي تدمج حتى الحد الأدنى من العوامل الاجتماعية بالفعل تحسينات ملحوظة. نقوم بإضفاء الطابع الرسمي على تصنيف سبع عوامل اجتماعية بناء على النظرية اللغوية وإثارة الإخفاقات الحالية والنجاحات الناشئة لكل منها. نقترح أن يتناول مجتمع NLP العوامل الاجتماعية للحصول على أقرب إلى هدف فهم اللغة يشبه الإنسان.
Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life. Still, it would be hard to argue that NLP models have reached human-level capacity. In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language's social factors. We show that current NLP systems systematically break down when faced with interpreting the social factors of language. This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance. At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements. We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them. We suggest that the NLP community address social factors to get closer to the goal of human-like language understanding.
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