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Register a new userArgument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
حجة الزوج استخراج مع توجيهات متبادلة الرسم البياني العلاقة بين الجملة
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تهدف استخراج الزوج للحجة (القرد) إلى استخراج أزواج الحجة التفاعلية من ممرتين من المناقشة. درس العمل السابق هذه المهمة في سياق مراجعة الأقران و Rebuttal، وتحللها في مهمة وضع علامة تسلسل ومهمة تصنيف علاقات الجملة. ومع ذلك، على الرغم من الأداء الواعد، فإن هذا النهج يحصل على أزواج الحجة ضمنيا من قبل المهامتين المتحلين، يفتقر إلى نمذجة صراحة لتفاعلات مستوى الوسيطة بين أزواج الحجة. في هذه الورقة، نقوم بمعالجة مهمة القرد من خلال إطار توجيه متبادل، والتي يمكن أن تستخدم معلومات حجة في مقطع واحد لتوجيه تحديد الحجج التي يمكن أن تشكل أزواج معها في مقطع آخر. وبهذه الطريقة، يمكن لمركزين توجه بعضهما البعض بشكل متبادل في عملية القرد. علاوة على ذلك، نقترح رسم بياني علاقة بين الجملة إلى النموذج بشكل فعال العلاقات بين الجملتين وبالتالي يسهل استخراج أزواج الحجة. يمكن أن تمثل طريقةنا المقترحة بشكل أفضل دلالات المستوى الكلي على مستوى الوسيطة، وبالتالي التقاط صراحة الارتباطات المعقدة بين أزواج الحجة. تظهر النتائج التجريبية أن نهجنا تتفوق بشكل كبير على النموذج الحالي للحالة الحالية.
Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.
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