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Register a new userHoney or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
العسل أو السم؟حل لعنة المشغل في الكشف عن الحدث قليلة عبر التدخل السببي
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تأثرت الكشف عن الحدث منذ فترة طويلة بسبب لعنة الزناد: التجاوز الزنجي سيضر بالقدرة على مستوى التعميم أثناء تقديره سيضر بأداء الكشف.هذه المشكلة أكثر حدة في سيناريو أقل لقطة.في هذه الورقة، نحدد وحل مشكلة لعنة المشغل في اكتشاف حدث قليل الطواف (FSED) من وجهة نظر سببية.من خلال صياغة FSED مع نموذج سببي هيكلي (SCM)، وجدنا أن الزناد هو مواجهة السياق والنتيجة، مما يجعل أساليب FSED السابقة أسهل بكثير على المشغلات المبكرة.لحل هذه المشكلة، نقترح التدخل في السياق عن طريق تعديل الخلفية أثناء التدريب.تبين التجارب أن طريقتنا تحسن بشكل كبير من FSED على كل من مجموعة بيانات ACE05 و Maven.
Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on both ACE05 and MAVEN datasets.
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We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes
. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.
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The task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in state-of-th
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