ActiveQA — IndicQA Telugu
This repository contains the trained artifacts for an Active Question Answering system.
Architecture
Original Question → Reformulator → Multiple Candidate Questions → QA Environment → Answer/F1 Reward → Selector → Best Reformulation
The reformulator is further optimized using REINFORCE with the QA performance as the reward.
Dataset
ai4bharat/IndicQA
Language: Telugu
Components
Reformulator
Generates alternative formulations of the original question.
QA Environment
Answers the original and reformulated questions using an extractive QA model.
Selector
Selects the candidate question expected to provide the best QA result.
RL
The reformulator receives the improvement in QA F1 as its reward.
Repository
nlpctx/activeqa-indicqa-telugu
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