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Register a new userPerceived and Intended Sarcasm Detection with Graph Attention Networks
اكتشاف السخرية المتصورة والمقصودة مع شبكات انتباه الرسوم البيانية
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تركز أنظمة الكشف عن الساركاز الموجودة على استغلال العلامات اللغوية أو السياق أو البثور على مستوى المستخدم. ومع ذلك، تشير الدراسات الاجتماعية إلى أن العلاقة بين المؤلف والجمهور يمكن أن تكون ذات صلة بنفس القدر لاستخدام السخرية وتفسيرها. في هذا العمل، نقترح إطارا بالاستفادة المشتركة (1) سياق مستخدم من تغريداتهم التاريخية مع (2) المعلومات الاجتماعية من حي المستخدم في رسم بياني تفاعل، إلى السياق تفسير المنشور. نحن نميز بين الهوية المتصورة والمبلغ عنها الذاتي. نستخدم شبكات انتباه الرسوم البيانية (GAT) عبر المستخدمين والتويت في مؤشر ترابط محادثة، جنبا إلى جنب مع العديد من تمثيلات سجل المستخدم كثيفة. بصرف النظر عن تحقيق نتائج حديثة على مجموعة البيانات التي تم نشرها مؤخرا من مستخدمي Twitter مؤخرا مع تغريدات ثلاثية العدد 30K، قم بإضافة تغريدات 10M Unabeled كسياق، تشير تجاربنا إلى أن شبكة الرسم البياني تساهم في تفسير النوايا الساخرة للمؤلف أكثر من للتنبؤ بتصور السخرية من قبل الآخرين.
Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user's neighborhood in an interaction graph, to contextualize the interpretation of the post. We distinguish between perceived and self-reported sarcasm identification. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with various dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our experiments indicate that the graph network contributes to interpreting the sarcastic intentions of the author more than to predicting the sarcasm perception by others.
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Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and inte
rpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user's conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.
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