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Register a new userDAGN: Discourse-Aware Graph Network for Logical Reasoning
Dagn: Converse-Aware Graph Network للمنطق المنطقي
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تتطلب QA مؤخرا مع أسئلة التفكير المنطقي علاقات على مستوى المرور بين الجمل.ومع ذلك، فإن النهج الحالية لا تزال تركز على العلاقات على مستوى الجملة تتفاعل بين الرموز.في هذا العمل، نستكشف عن أدلة على مستوى المرور التجميعي لحل ضمنيا المنطق المنطقي باستخدام المعلومات المستندة إلى الخطاب.نقترح شبكة الرسم البياني على داجرا (DAGN) تلك الأسباب التي تعتمد على هيكل الخطاب للنصوص.يرميز النموذج معلومات الخطاب كشركة رسم بياني مع وحدات الخطاب الأولية (EDUS) وعلاقات الخطاب، وتعلم ميزات Converse-Aware عبر شبكة رسم بياني لمهام QA المصب.يتم إجراء التجارب على اثنين من مجموعات البيانات المنطقية من المنطقية، reclor and logiqa، ونتائج dagn المقترحة لدينا نتائج تنافسية.يتوفر شفرة المصدر في https://github.com/eleanor-h/dagn.
Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting among tokens. In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information. We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts. The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks. Experiments are conducted on two logical reasoning QA datasets, ReClor and LogiQA, and our proposed DAGN achieves competitive results. The source code is available at https://github.com/Eleanor-H/DAGN.
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