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Register a new userMachine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction
آنية قراءة الفهم مع زيادة البيانات: دراسة حالة حول استخراج وسيطة الحدث الضمني
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إن استخراج وسيطة الحدث الضمني (EAE) هي مهمة حاسمة لاستخراج المعلومات على مستوى المستندات تهدف إلى تحديد حجج الحدث بما يتجاوز مستوى الجملة.على الرغم من الجهود العديدة لهذه المهمة، فإن عدم وجود بيانات تدريبية كافية قد أعاقت الدراسة.في هذه الورقة، نأخذ منظورا جديدا لمعالجة قضية Sparsity الخاصة بالبيانات التي تواجهها EAE الضمنية، من خلال سد المهمة مع فهم القراءة بالآلة (MRC).على وجه الخصوص، نحن ابتكرت نظاميين تكبير البيانات عبر MRC، بما في ذلك: 1) يتيح نقل المعرفة الضمني، مما يتيح نقل المعرفة من المهام الأخرى، من خلال بناء إطار تدريب موحد في صياغة MRC، و 2) تكبير بيانات صريح، والتي يمكن أن تولد جديدا جديداأمثلة تدريبية، عن طريق علاج نماذج MRC كهندان.لقد بررت التجارب الواسعة فعالية نهجنا - - لا يحصل فقط على أداء حديثة على معيارين، ولكن أيضا يوضح نتائج متفوقة في سيناريو منخفضة البيانات.
Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded the study. In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator. The extensive experiments have justified the effectiveness of our approach --- it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.
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