24 citations · 44 across the 15 of their papers we have counts for
8 papers · 1 filter
Can Transformers Reason About Effects of Actions?
Pratyay Banerjee, Chitta Baral, Man Luo +4
A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditi…
WeaQA: Weak Supervision via Captions for Visual Question Answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang +1
Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated \textit{Image-Question-Answer} (I-Q-A) triplets. This has…
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
Tejas Gokhale, Pratyay Banerjee, Chitta Baral +1
While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, eva…
Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering
Pratyay Banerjee, Chitta Baral
The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such…
Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering
Pratyay Banerjee, Chitta Baral
Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recentl…
Natural Language QA Approaches using Reasoning with External Knowledge
Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal +1
Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's ``councilmen'' example in his 1972 paper and McCarthy's Mr. Hug…