Search Results for author: Kuntal Kumar Pal

Found 14 papers, 5 papers with code

Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users

no code implementations4 Jul 2023 Kuntal Kumar Pal, Michael Gamon, Nirupama Chandrasekaran, Silviu Cucerzan

To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities.

Community Question Answering Retrieval +1

EDM3: Event Detection as Multi-task Text Generation

1 code implementation25 May 2023 Ujjwala Anantheswaran, Himanshu Gupta, Mihir Parmar, Kuntal Kumar Pal, Chitta Baral

We show that EDM3 helps to learn transferable knowledge that can be leveraged to perform Event Detection and its subtasks concurrently, mitigating the error propagation inherent in pipelined approaches.

Event Detection Sentence +1

Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums

no code implementations5 Mar 2023 Kazuaki Kashihara, Kuntal Kumar Pal, Chitta Baral, Robert P Trevino

We propose a method called Next Paragraph Prediction with Instructional Prompting (NPP-IP) to predict thread structures while grounded on the context around posts.

Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model

1 code implementation Findings (EMNLP) 2021 Kuntal Kumar Pal, Chitta Baral

Some possible reasons can be the tokenizers and pre-training objectives which are not specifically designed to learn and preserve numeracy.

Transfer Learning

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

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

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

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

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

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