Summary of Improve Academic Query Resolution Through Bert-based Question Extraction From Images, by Nidhi Kamal et al.
Improve Academic Query Resolution through BERT-based Question Extraction from Images
by Nidhi Kamal, Saurabh Yadav, Jorawar Singh, Aditi Avasthi
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach for resolving students’ queries in Edtech settings, leveraging a BERT-based deep learning model. The proposed method addresses the limitations of existing solutions, which struggle with complex image-based queries containing multiple questions or textual noise. By comparing its performance to rule-based and layout-based methods, this study aims to enhance the accuracy and efficiency of student query resolution in Edtech organizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edtech companies want to help students quickly get answers to their questions. One way they do this is by using chatbots that let students ask questions easily. But when students use images instead of typing, it gets tricky. Images can have multiple questions or random text that makes it hard for computers to answer correctly. This paper suggests a new way to extract questions from images or text using a special deep learning model based on BERT. The goal is to make it easier and more accurate for Edtech companies to help students get the answers they need. |
Keywords
» Artificial intelligence » Bert » Deep learning
