no code implementations • 5 Jun 2023 • Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas
As a step towards this goal, we focus on geometry priors and introduce LatFormer, a model that incorporates lattice symmetry priors in attention masks.
no code implementations • 19 May 2023 • Mattia Atzeni, Mikhail Plekhanov, Frédéric A. Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
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 • NeurIPS 2021 • Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas
State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs.
no code implementations • ICLR 2022 • Mattia Atzeni, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan
Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.
Out-of-Distribution Generalization reinforcement-learning +2
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 • 8 May 2021 • Evgeny Krivosheev, Mattia Atzeni, Katsiaryna Mirylenka, Paolo Scotton, Christoph Miksovic, Anton Zorin
Data integration has been studied extensively for decades and approached from different angles.
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 • 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.
no code implementations • 17 Jan 2020 • Evgeny Krivosheev, Mattia Atzeni, Katsiaryna Mirylenka, Paolo Scotton, Fabio Casati
In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles.