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Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability
Riya Sawhney, Samrat Yadav, Indrajit Bhattacharya +1
Real-world applications of KBQA require models to handle unanswerable questions with a limited volume of in-domain labeled training data. We propose the novel task of few-shot tran…
RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions
Prayushi Faldu, Indrajit Bhattacharya, Mausam
An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. How…
Adapting Pre-trained Generative Models for Extractive Question Answering
Prabir Mallick, Tapas Nayak, Indrajit Bhattacharya
Pre-trained Generative models such as BART, T5, etc. have gained prominence as a preferred method for text generation in various natural language processing tasks, including abstra…
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
Mayur Patidar, Riya Sawhney, Avinash Singh +3
Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data, which make them costly and time-consuming to deploy. We introduce the problem of few-…
Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?
Pratik Saini, Tapas Nayak, Indrajit Bhattacharya
Two distinct approaches have been proposed for relational triple extraction - pipeline and joint. Joint models, which capture interactions across triples, are the more recent devel…