17 citations · 101 across the 17 of their papers we have counts for
18 papers
MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time
Peter Phan, Dhruv Agarwal, Kavitha Srinivas +3
Large language models (LLMs) are increasingly being applied to black-box optimization tasks, from program synthesis to molecule design. Prior work typically leverages in-context le…
Learning to Guide a Saturation-Based Theorem Prover
Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni +8
Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the…
Logic Embeddings for Complex Query Answering
Francois Luus, Prithviraj Sen, Pavan Kapanipathi +4
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…
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar +27
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understandin…
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi +6
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 deci…
Reading Comprehension as Natural Language Inference: A Semantic Analysis
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited…
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
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. However, t…
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi +6
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowle…
Type-augmented Relation Prediction in Knowledge Graphs
Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula +2
Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entit…
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla +3
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. This…
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel +10
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approa…
Path-Based Contextualization of Knowledge Graphs for Textual Entailment
Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi +3
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 fro…
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…
High-Fidelity Vector Space Models of Structured Data
Maxwell Crouse, Achille Fokoue, Maria Chang +6
Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most mach…
Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Ryan Musa, Xiaoyan Wang, Achille Fokoue +6
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. Much o…
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa +8
Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained…
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
Michael Boratko, Harshit Padigela, Divyendra Mikkilineni +10
The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set…
A Measure for Dialog Complexity and its Application in Streamlining Service Operations
Q Vera Liao, Biplav Srivastava, Pavan Kapanipathi
Dialog is a natural modality for interaction between customers and businesses in the service industry. As customers call up the service provider, their interactions may be routine…