activity
20172021
most citedEnhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

17 citations · 42 across the 8 of their papers we have counts for

collaborators

17 papers

cs.AI2021

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…

cs.AI20217 cited

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…

cs.CL2020

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…

cs.AI2020

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…

cs.CL20204 cited

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…

cs.CL20204 cited

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…