Open SiddharthKadam opened 2 months ago
Suggested Changes:
Please try to answer these questions by tomorrow.
Changes have been updated for comments 1 and 2. Regarding the evaluation, I have a doubt and have proposed the following strategy in the attached file. Let me know if you'd like further adjustments!
Evaluation needs some more thoughts. It is possible to have very similar meaning of two questions without them having a lexical overlap. In which case your approach will penalize it unfairly. On the other end, "what is an iPhone?" and "is iPhone a what?" have perfect similarity but the latter is not useful. You should look for a more robust evaluation metric. Also the proposed paper, although good, is a demo paper so there is no evalaution conducted. Did any of the other two papers have evalution and results? If yes, then you can get some idea about how to perform evalution from those papers.
One possible approach is to use METEOR (Metric for Evaluation of Translation with Explicit Ordering).
Referred research paper : https://link.springer.com/article/10.1007/s13748-023-00295-9
For further clarification, you can refer to the explanation video here: https://www.youtube.com/watch?v=FqQbrlEh_b0&t=421s
METEOR is a good metric. You might also want to look at BertScore.
I think the proposal is in a good shape now. I am accepting it.
Title
Automated Question Generation for Enhanced Learning
Team Name
Chaotic Noobs
Email
202318015@daiict.ac.in
Team Member 1 Name
Siddharth Kadam
Team Member 1 Id
202318015
Team Member 2 Name
Taruna Mati
Team Member 2 Id
202318045
Team Member 3 Name
Ananya Adarsh
Team Member 3 Id
202318027
Team Member 4 Name
Asma Narmawala
Team Member 4 Id
202318025
Category
Reproducibility
Problem Statement
Develop a system for Automatic Question Generation (AQG) using Natural Language Processing (NLP) techniques to generate questions from a given text. The system will aim to generate a variety of question types (e.g., who, what, where, when, why, how), focusing on the semantic and syntactic analysis of sentences to ensure relevant and grammatically correct questions. The key challenge will be to improve the system's ability to handle complex sentence structures and enhance the quality of generated questions in terms of relevance and fluency.
Evaluation Strategy
METEOR,BERT SCORE
Dataset
Dataset Name : SQuAD 1.0 Dataset Link : https://rajpurkar.github.io/SQuAD-explorer/
Resources
Paper Title - ParaQG: A System for Generating Questions and Answers from Paragraphs Paper Link - https://arxiv.org/abs/1909.01642