Challenge closed-book science exam

Challenge closed-book science exam

Problem being addressed

Standardized science exam is a challenging AI task for question answering​,​ as it requires rich scientific and commonsense knowledge.

Solution

A model-agnostic meta-learning method to train a meta-classifier which is able to capture the meta-information that contains similar features among different tasks, so that the meta-classifier can quickly adapt to new tasks with few samples. The questions are regarded with the same knowledge points as a task. The model learns to reason testing questions with the assistance of question labels and example questions (examine the same knowledge points) given by the meta-classifier. Experiments show that the proposed method achieves impressive performance on the closed-book exam with the help of the few-shot classification information.

Advantages of this solution

Experimental results show that the proposed method greatly improves the QA accuracy by +17.6%, and exceeds the performance of retrieval-based QA method.

Solution originally applied in these industries

education

Education Sector

Possible New Application of the Work

data:image/png;base64,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

Electronics and Sensors Industry

Meta learning helps to execute training when large training datasets aren't available. For example, a mini robot completes the desired task on an uphill surface during test even through it was only trained in a flat surface environment.

/static/media/finance.4b3df9f1.png

Financial Sector

Meta learning can be used to detect fraudulent transactions based on other tasks which the low-level models were trained on.

Author of original research described in this blitzcard:

question-mark-icon

Name of the author who conducted the original research that this blitzcard is based on.

Source URL: #############check-icon


search-iconBrowse all blitzcards