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Register a new userDisentangling Online Chats with DAG-structured LSTMs
DEVENTANGLING CHATS عبر الإنترنت مع LSTMS منظم DAG
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تتيح العديد من أنظمة المراسلة الحديثة الاتصالات النصية سريعة ومتزامنة بين العديد من المستخدمين. تسلسل الرسائل الناتجة يخفي بنية أكثر تعقيدا في محادثات فرعية مستقلة متشابكة مع بعضها البعض. يشكل هذا تحديا لأي مهمة تهدف إلى فهم محتوى سجلات الدردشة أو جمع المعلومات منها. تتمثل القدرة على فك هذه المحادثات بعد ذلك بمثابة نجاح العديد من المهام المصب مثل التلخيص والإجابة على الأسئلة. يتم استخدام المعلومات المهيكلة المصاحبة للنص مثل بدوره المستخدم، يذكر المستخدم، الطوابع الطوابع الطوابع الطوابع الطوابع الطوابع الطوابع الطوابع الطوابع الزمنية، كإجراء يقدم من قبل المشاركين أنفسهم الذين يحتاجون إلى اتباع المحادثة ويبضاهم مهمين للإجراءات. تعد DAG-LSTMS، وتعميم الأشجار LSTMS التي يمكن أن تتعامل مع تبعيات أنديكليكوس الموجهة، وسيلة طبيعية لإدماج هذه المعلومات وطبيعتها غير المتسلسلة. في هذه الورقة، نطبق DAG-LSTMS بمهمة محادثة محادثة المحادثة. نحن نقوم بإجراء تجاربنا على مجموعة بيانات أوبونتو IRC. نظرا لأن النموذج الجديد الذي نقترحه يحقق حالة الوضع الفني في مهمة استعادة العلاقات الردية وتنافسية على مقاييس المنظمات الأخرى.
Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics.
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