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Register a new userMultitask Learning for Citation Purpose Classification
التعدد المتعدد التعلم لتصنيف الغرض من الاقتباس
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نقدم دخولنا إلى تصنيف سياق استشهاد المهام المشترك 2021 3C بناء على منافسة الغرض.الهدف من المسابقة هو تصنيف الاقتباس في مادة علمية بناء على هدفها.هذه المهمة مهمة لأنه من المحتمل أن تؤدي إلى طرق أكثر شمولا لتلخيص الغرض واستخدامات المقالات العلمية، ولكن من الصعب أيضا، ويرجع ذلك أساسا إلى كمية محدودة من البيانات التدريبية المتاحة التي كانت فيها أغراض كل الاقتباس يدوياالمسمى، جنبا إلى جنب مع الذاتية لهذه الملصقات.إن دخولنا في المسابقة هو نموذج متعدد المهام يجمع بين وحدات متعددة مصممة للتعامل مع المشكلة من وجهات نظر مختلفة، بما في ذلك الميزات اللغوية التي تم إنشاؤها يدويا، وميزات TF-IDF، ونموذج LSTM- مع الانتباه.كما نقدم دراسة الاجتثاث والتحليل الميزات التي يمكن أن تؤدي رؤيتها إلى العمل في المستقبل.
We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with- attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.
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This paper describes our system (IREL) for 3C-Citation Context Classification shared task of the Scholarly Document Processing Workshop at NAACL 2021. We participated in both subtask A and subtask B. Our best system achieved a Macro F1 score of 0.269
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We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ gen
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This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and
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