no code implementations • 4 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.
1 code implementation • 25 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.
no code implementations • 5 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.
no code implementations • 20 Feb 2023 • Kuntal Kumar Pal, Kazuaki Kashihara, Ujjwala Anantheswaran, Kirby C. Kuznia, Siddhesh Jagtap, Chitta Baral
We also show that with a few examples, UTS can be adapted to novel unseen tasks and the nature of data
1 code implementation • 14 Oct 2022 • Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Scaria, Siddharth Goyal, Chitta Baral
We show that even state-of-the-art models such as GPT-3, GPT-2, and T5 struggle to answer the feasibility questions correctly.
10 code implementations • 16 Apr 2022 • Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, Daniel Khashabi
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
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.
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 • 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 • 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 • 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 • 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 #27 on
Question Answering
on OpenBookQA