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Register a new userLabel-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification
التمثيل التعليمي التسلسل الهرمي لتعزيز التسمية لتحديد استعارة متسلسل
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نهج تحديد استعارة الحديثة النظر أساسا في ميزات النص السياقية في غضون جملة أو إدخال ميزات لغوية خارجية إلى النموذج. لكنهم عادة ما يتجاهلون المعلومات الإضافية التي يمكن أن توفرها البيانات، مثل معلومات الاستعارة السياقية ومعلومات الخطاب الأوسع نطاقا. في هذه الورقة، نقترح نموذجا تم تزويده بتمثيل سياقي هرمي لاستخراج مزيد من المعلومات من كل من مستوى الجملة ومستوى الخطاب. على مستوى الجملة، نستفيد من معلومات الاستعارة من الكلمات التي تبادل الكلمة المستهدفة في الجملة لتعزيز قدرة المنطق على نموذجنا عبر تمثيل محسن محسن على الملصقات. عند مستوى الخطاب، يتم اعتماد شبكة الذاكرة العالمية التي تدركها لتعلم التبعية بعيدة المدى بين نفس الكلمات داخل خطاب. أخيرا، يجمع نموذجنا بين التمثيلات التي تم الحصول عليها من هذين الجزأين. تظهر نتائج التجربة على مهمتين لمجموعة بيانات VUA أن طرازنا يتفوق على كل طريقة أخرى لا تستخدم أي معرفة خارجية أيضا باستثناء ما يحتوي نموذج اللغة المدربة مسبقا عليه.
Recent metaphor identification approaches mainly consider the contextual text features within a sentence or introduce external linguistic features to the model. But they usually ignore the extra information that the data can provide, such as the contextual metaphor information and broader discourse information. In this paper, we propose a model augmented with hierarchical contextualized representation to extract more information from both sentence-level and discourse-level. At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation. At the discourse level, the position-aware global memory network is adopted to learn long-range dependency among the same words within a discourse. Finally, our model combines the representations obtained from these two parts. The experiment results on two tasks of the VUA dataset show that our model outperforms every other state-of-the-art method that also does not use any external knowledge except what the pre-trained language model contains.
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