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المتمردين: استخراج العلاقة حسب جيل لغة نهاية إلى نهاية
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تستخرف تستخرف توائم النص من النص الخام مهمة حاسمة في استخراج المعلومات، مما يتيح تطبيقات متعددة مثل ملء قواعد المعرفة أو التحقق من صحة المعرفة ومهام المصب الأخرى. ومع ذلك، فإنه عادة ما ينطوي عادة على خطوط أنابيب متعددة الخطوات التي تنتشر أخطاء أو تقتصر على عدد صغير من أنواع العلاقات. للتغلب على هذه القضايا، نقترح استخدام نماذج SEQ2SEQ AutoRegressive. لقد سبق أن ثبت أن هذه النماذج قد تؤدي بشكل جيد ليس فقط في توليد اللغة، ولكن أيضا في مهام NLU مثل ربط الكيان، بفضل تأطيرها كامرأة SEQ2SEQ. في هذه الورقة، نظهر كيف يمكن تبسيط استخراج العلاقات من خلال التعبير عن توابع توائم كسلسلة من النص، ونحن نقدم المتمردين، نموذج SEQ2SEQ يعتمد على BART يؤدي استخراج العلاقات الطرفية إلى نهاية لأكثر من 200 نوع من العلاقات المختلفة. نظهر مرونة نموذجنا من خلال ضبطه بشكل جيد على مجموعة من معايير استخراج العلاقات وعلاقة التصنيف، مع أنها تحقق أداء حديثة في معظمها.
Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model's flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.
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