2 code implementations • Findings (NAACL) 2022 • G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos, Alexander Gray, L Venkata Subramaniam
Interestingly, we discovered that BLINK exhibits diminishing returns, i. e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance.
no code implementations • 23 Feb 2024 • Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
no code implementations • 12 Oct 2023 • Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis Lastras
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
1 code implementation • 5 Jul 2023 • Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
no code implementations • 18 Jun 2023 • Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
no code implementations • 31 May 2023 • Maxwell Crouse, Ramon Astudillo, Tahira Naseem, Subhajit Chaudhury, Pavan Kapanipathi, Salim Roukos, Alexander Gray
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser.
no code implementations • 7 May 2023 • Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Astudillo, Achille Fokoue, Tim Klinger
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion.
no code implementations • 18 Apr 2022 • Dung Thai, Srinivas Ravishankar, Ibrahim Abdelaziz, Mudit Chaudhary, Nandana Mihindukulasooriya, Tahira Naseem, Rajarshi Das, Pavan Kapanipathi, Achille Fokoue, Andrew McCallum
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
no code implementations • 15 Jan 2022 • Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L Venkata Subramaniam
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
no code implementations • 15 Dec 2021 • Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Pavan Kapanipathi, Radu Florian, Salim Roukos
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
no code implementations • AAAI Workshop CLeaR 2022 • Kinjal Basu, Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Tim Klinger, Murray Campbell, Mrinmaya Sachan, Gopal Gupta
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming Natural Language Understanding +2
no code implementations • 10 Nov 2021 • Srinivas Ravishankar, June Thai, Ibrahim Abdelaziz, Nandana Mihidukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue
Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.
no code implementations • 28 Sep 2021 • Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme LimaRyan Riegel, Francois Luus, L Venkata Subramaniam
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
no code implementations • 16 Sep 2021 • Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Francois Luus, Salim Roukos, Alexander Gray
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.
no code implementations • 16 Aug 2021 • Gaetano Rossiello, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Mihaela Bornea, Alfio Gliozzo, Tahira Naseem, Pavan Kapanipathi
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on Relation Linking on QALD-9
no code implementations • ACL 2021 • Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL).
no code implementations • ACL 2021 • Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
Relation linking is a crucial component of Knowledge Base Question Answering systems.
no code implementations • 7 Jun 2021 • Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
no code implementations • NAACL 2021 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
4 code implementations • 28 Feb 2021 • Francois Luus, Prithviraj Sen, Pavan Kapanipathi, Ryan Riegel, Ndivhuwo Makondo, Thabang Lebese, Alexander Gray
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables.
1 code implementation • Findings (ACL) 2021 • Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
no code implementations • Joint Conference on Lexical and Computational Semantics 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
2 code implementations • 8 Oct 2020 • Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making.
Ranked #1 on Commonsense Reasoning for RL on commonsense-rl
no code implementations • 4 Oct 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
no code implementations • 18 Sep 2020 • Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
no code implementations • 16 Sep 2020 • Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji
Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones.
1 code implementation • 16 Sep 2020 • Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Mo Yu, Alfio Gliozzo, Salim Roukos, Alexander Gray
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on Relation Linking on QALD-7
no code implementations • 12 Jul 2020 • Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.
no code implementations • 2 May 2020 • Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla, Mrinmaya Sachan, Pavan Kapanipathi, Kartik Talamadupula
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.
1 code implementation • 5 Nov 2019 • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
no code implementations • 5 Nov 2019 • Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue
In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.
no code implementations • 5 Nov 2019 • Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
no code implementations • 9 Jan 2019 • Maxwell Crouse, Achille Fokoue, Maria Chang, Pavan Kapanipathi, Ryan Musa, Constantine Nakos, Lingfei Wu, Kenneth Forbus, Michael Witbrock
Machine learning systems regularly deal with structured data in real-world applications.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
no code implementations • AKBC 2019 • Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
no code implementations • EMNLP 2018 • Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock
Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set.
no code implementations • 15 Sep 2018 • Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
no code implementations • 15 Sep 2018 • Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
no code implementations • WS 2018 • Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock
We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset.
no code implementations • 4 Aug 2017 • Q. Vera Liao, Biplav Srivastava, Pavan Kapanipathi
Dialog is a natural modality for interaction between customers and businesses in the service industry.