Search Results for author: Pratyay Banerjee

Found 27 papers, 10 papers with code

To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo

1 code implementation ACL 2022 Yiran Luo, Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

We find that the original Who’s Waldo dataset compiled for this task contains a large number of biased samples that are solvable simply by heuristic methods; for instance, in many cases the first name in the sentence corresponds to the largest bounding box, or the sequence of names in the sentence corresponds to an exact left-to-right order in the image.

Benchmarking Person-centric Visual Grounding +1

Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models

no code implementations2 Oct 2023 Man Luo, Shrinidhi Kumbhar, Ming Shen, Mihir Parmar, Neeraj Varshney, Pratyay Banerjee, Somak Aditya, Chitta Baral

This work strives to understand the proficiency of LLMs in logical reasoning by offering a brief review of the latest progress in this area; with a focus on the logical reasoning datasets, tasks, and the methods adopted to utilize LLMs for reasoning.

Knowledge Distillation Language Modelling +1

Lexi: Self-Supervised Learning of the UI Language

1 code implementation23 Jan 2023 Pratyay Banerjee, Shweti Mahajan, Kushal Arora, Chitta Baral, Oriana Riva

Along with text, these resources include visual content such as UI screenshots and images of application icons referenced in the text.

Image Retrieval Language Modelling +2

To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo

1 code implementation30 Mar 2022 Yiran Luo, Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

We find that the original Who's Waldo dataset compiled for this task contains a large number of biased samples that are solvable simply by heuristic methods; for instance, in many cases the first name in the sentence corresponds to the largest bounding box, or the sequence of names in the sentence corresponds to an exact left-to-right order in the image.

Benchmarking Person-centric Visual Grounding +1

Unsupervised Natural Language Inference Using PHL Triplet Generation

1 code implementation Findings (ACL) 2022 Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, Chitta Baral

Transformer-based models achieve impressive performance on numerous Natural Language Inference (NLI) benchmarks when trained on respective training datasets.

Natural Language Inference Sentence

Semantically Distributed Robust Optimization for Vision-and-Language Inference

1 code implementation Findings (ACL) 2022 Tejas Gokhale, Abhishek Chaudhary, Pratyay Banerjee, Chitta Baral, Yezhou Yang

Analysis of vision-and-language models has revealed their brittleness under linguistic phenomena such as paraphrasing, negation, textual entailment, and word substitutions with synonyms or antonyms.

Data Augmentation Natural Language Inference +2

Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering

1 code implementation EMNLP 2021 Man Luo, Yankai Zeng, Pratyay Banerjee, Chitta Baral

The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge.

Question Answering Retrieval +1

Weakly Supervised Relative Spatial Reasoning for Visual Question Answering

no code implementations ICCV 2021 Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

In this work, we evaluate the faithfulness of V\&L models to such geometric understanding, by formulating the prediction of pair-wise relative locations of objects as a classification as well as a regression task.

Question Answering Visual Question Answering +1

Commonsense Reasoning with Implicit Knowledge in Natural Language

no code implementations AKBC 2021 Pratyay Banerjee, Swaroop Mishra, Kuntal Kumar Pal, Arindam Mitra, Chitta Baral

Two common approaches to this are (i) Use of well-structured commonsense present in knowledge graphs, and (ii) Use of progressively larger transformer language models.

Knowledge Graphs

Constructing Flow Graphs from Procedural Cybersecurity Texts

1 code implementation Findings (ACL) 2021 Kuntal Kumar Pal, Kazuaki Kashihara, Pratyay Banerjee, Swaroop Mishra, Ruoyu Wang, Chitta Baral

We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures.

Sentence Sentence Embeddings

Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction

no code implementations ACL 2021 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.

Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling

no code implementations23 Mar 2021 Pratyay Banerjee, Kuntal Kumar Pal, Fish Wang, Chitta Baral

Inspired by recent advances in natural language processing, we propose a novel solution to infer variable names in decompiled code based on Masked Language Modeling, Byte-Pair Encoding, and neural architectures such as Transformers and BERT.

Language Modelling Masked Language Modeling

Self-Supervised Test-Time Learning for Reading Comprehension

no code implementations NAACL 2021 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 supervised methods.

Question Answering Reading Comprehension

Can Transformers Reason About Effects of Actions?

no code implementations17 Dec 2020 Pratyay Banerjee, Chitta Baral, Man Luo, Arindam Mitra, Kuntal Pal, Tran C. Son, Neeraj Varshney

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 conditions implying a conclusion.

Common Sense Reasoning Question Answering

WeaQA: Weak Supervision via Captions for Visual Question Answering

no code implementations Findings (ACL) 2021 Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated \textit{Image-Question-Answer} (I-Q-A) triplets.

Question Answering Visual Question Answering

MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering

2 code implementations EMNLP 2020 Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang

In this paper, we present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.

Out-of-Distribution Generalization Question Answering +1

Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering

no code implementations7 Apr 2020 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.

Information Retrieval Open-Domain Question Answering +1

Natural Language QA Approaches using Reasoning with External Knowledge

no code implementations6 Mar 2020 Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra

The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront.

Question Answering

VQA-LOL: Visual Question Answering under the Lens of Logic

no code implementations ECCV 2020 Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang

We propose our {Lens of Logic (LOL)} model which uses question-attention and logic-attention to understand logical connectives in the question, and a novel Fr\'echet-Compatibility Loss, which ensures that the answers of the component questions and the composed question are consistent with the inferred logical operation.

Negation Question Answering +2

Knowledge Guided Named Entity Recognition for BioMedical Text

no code implementations10 Nov 2019 Pratyay Banerjee, Kuntal Kumar Pal, Murthy Devarakonda, Chitta Baral

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 entity types with the tags.

named-entity-recognition Named Entity Recognition +2

How Additional Knowledge can Improve Natural Language Commonsense Question Answering?

no code implementations19 Sep 2019 Arindam Mitra, Pratyay Banerjee, Kuntal Kumar Pal, Swaroop Mishra, Chitta Baral

Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role.

Language Modelling Multiple-choice +1

ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking

no code implementations WS 2019 Pratyay Banerjee

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task.

Learning-To-Rank Re-Ranking

Careful Selection of Knowledge to solve Open Book Question Answering

no code implementations ACL 2019 Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra, Chitta Baral

Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic.

Information Retrieval Question Answering +2

Cannot find the paper you are looking for? You can Submit a new open access paper.