32 citations · 109 across the 23 of their papers we have counts for
26 papers
Examining the Implications of Deepfakes for Election Integrity
Hriday Ranka, Mokshit Surana, Neel Kothari +7
It is becoming cheaper to launch disinformation operations at scale using AI-generated content, in particular 'deepfake' technology. We have observed instances of deepfakes in poli…
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models
Man Luo, Shrinidhi Kumbhar, Ming shen +5
Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and…
Lexi: Self-Supervised Learning of the UI Language
Pratyay Banerjee, Shweti Mahajan, Kushal Arora +2
Humans can learn to operate the user interface (UI) of an application by reading an instruction manual or how-to guide. Along with text, these resources include visual content such…
Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task
Shailaja Keyur Sampat, Pratyay Banerjee, Yezhou Yang +1
'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning…
Learning Action-Effect Dynamics from Pairs of Scene-graphs
Shailaja Keyur Sampat, Pratyay Banerjee, Yezhou Yang +1
'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning…
To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo
Yiran Luo, Pratyay Banerjee, Tejas Gokhale +2
We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who's Waldo dataset. Given an image and a caption, PCVG…
Unsupervised Natural Language Inference Using PHL Triplet Generation
Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale +1
Transformer-based models achieve impressive performance on numerous Natural Language Inference (NLI) benchmarks when trained on respective training datasets. However, in certain ca…
Semantically Distributed Robust Optimization for Vision-and-Language Inference
Tejas Gokhale, Abhishek Chaudhary, Pratyay Banerjee +2
Analysis of vision-and-language models has revealed their brittleness under linguistic phenomena such as paraphrasing, negation, textual entailment, and word substitutions with syn…
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
Man Luo, Yankai Zeng, Pratyay Banerjee +1
Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evalua…
Weakly Supervised Relative Spatial Reasoning for Visual Question Answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang +1
Vision-and-language (V\&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the…
Constructing Flow Graphs from Procedural Cybersecurity Texts
Kuntal Kumar Pal, Kazuaki Kashihara, Pratyay Banerjee +3
Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to comple…
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
Ming Shen, Pratyay Banerjee, Chitta Baral
In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pre-tr…
Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling
Pratyay Banerjee, Kuntal Kumar Pal, Fish Wang +1
Decompilation is the procedure of transforming binary programs into a high-level representation, such as source code, for human analysts to examine. While modern decompilers can re…
Self-Supervised Test-Time Learning for Reading Comprehension
Pratyay Banerjee, Tejas Gokhale, Chitta Baral
Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with s…
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…
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee +2
Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the…
VQA-LOL: Visual Question Answering under the Lens of Logic
Tejas Gokhale, Pratyay Banerjee, Chitta Baral +1
Logical connectives and their implications on the meaning of a natural language sentence are a fundamental aspect of understanding. In this paper, we investigate whether visual que…
Knowledge Guided Named Entity Recognition for BioMedical Text
Pratyay Banerjee, Kuntal Kumar Pal, Murthy Devarakonda +1
In this work, we formulate the NER task as a multi-answer knowledge guided QA task (KGQA) which helps to predict entities only by assigning B, I and O tags without associating enti…
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking
Pratyay Banerjee
In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regenera…