32 citations · 109 across the 23 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
Arindam Mitra, Pratyay Banerjee, Kuntal Kumar Pal +2
Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role. Recent language…
Careful Selection of Knowledge to solve Open Book Question Answering
Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra +1
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 kn…