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.
no code implementations • 2 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.
1 code implementation • 23 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.
no code implementations • 7 Dec 2022 • Shailaja Keyur Sampat, Pratyay Banerjee, Yezhou Yang, Chitta Baral
'Actions' play a vital role in how humans interact with the world.
1 code implementation • 7 Dec 2022 • Shailaja Keyur Sampat, Pratyay Banerjee, Yezhou Yang, Chitta Baral
'Actions' play a vital role in how humans interact with the world.
1 code implementation • 30 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.
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.
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.
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.
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.
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.
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.
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.
no code implementations • 23 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.
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.
no code implementations • 17 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.
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.
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.
no code implementations • EMNLP 2020 • Pratyay Banerjee, Chitta Baral
The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions.
no code implementations • 7 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.
2 code implementations • EMNLP 2020 • Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang
In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the scene.
no code implementations • 6 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.
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.
no code implementations • 10 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.
no code implementations • 19 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.
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.
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.
Ranked #23 on Question Answering on OpenBookQA