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شبكة مجانية قائمة على الرسم البياني مع تمثيلات سياقية من المنعطفات في الحوار
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تهدف استخراج العلاقات القائم على الحوار (إعادة) إلى استخراج العلاقة بين الحججتين التي تظهر في حوار. نظرا لأن الحوارات لديها خصائص حوادث الضمير الشخصية العالية وكثافة المعلومات المنخفضة، وبما أن معظم الحقائق العلائقية في الحوارات لا تدعمها أي جملة واحدة، فإن استخراج العلاقات القائمة على الحوار يتطلب فهم شامل للحوار. في هذه الورقة، نقترح Network Network Commany Commany Computal Network (Tucore-GCN) على غرار الاهتمام بالطريقة التي يفهم بها الناس الحوارات. بالإضافة إلى ذلك، نقترح نهج رواية يعامل مهمة الاعتراف بالمحادثات في المحادثات (ERC) كإعادة حوار قائما. تثبت التجارب في DataSet مقصورة الحوار وثلاث مجموعات بيانات ERC أن طرازنا فعال للغاية في مهام فهم اللغة الطبيعية القائمة على الحوار. في هذه التجارب، تتفوق Tucore-GCN على النماذج الحديثة على معظم مجموعات البيانات القياسية. يتوفر الكود الخاص بنا في https://github.com/blacknoodle/tucore-gcn.
Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.
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