no code implementations • ACL (dialdoc) 2021 • Xi Chen, Faner Lin, Yeju Zhou, Kaixin Ma, Jonathan Francis, Eric Nyberg, Alessandro Oltramari
In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation.
no code implementations • 23 Nov 2024 • Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paassen, Sascha Saralajew, Ute Schmid, Michael Biehl, Marianna Bolognesi, Xin Luna Dong, Kiril Gashteovski, Pascal Hitzler, Giuseppe Marra, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann
A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
no code implementations • 17 Aug 2024 • Siyu Wu, Alessandro Oltramari, Jonathan Francis, C. Lee Giles, Frank E. Ritter
Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems.
no code implementations • 8 Jul 2024 • Aaron Lohner, Francesco Compagno, Jonathan Francis, Alessandro Oltramari
This representation of a traffic scene is referred to as a scene graph, and can be used as input for an accident classifier.
no code implementations • 13 Nov 2023 • Alessandro Oltramari
High-level reasoning can be defined as the capability to generalize over knowledge acquired via experience, and to exhibit robust behavior in novel situations.
no code implementations • 18 Jul 2023 • Ehsan Qasemi, Jonathan M. Francis, Alessandro Oltramari
Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Intelligent Transportation Systems.
1 code implementation • 5 Jun 2023 • Jiarui Zhang, Filip Ilievski, Kaixin Ma, Aravinda Kollaa, Jonathan Francis, Alessandro Oltramari
Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure.
1 code implementation • 4 Dec 2022 • Jiarui Zhang, Filip Ilievski, Aravinda Kollaa, Jonathan Francis, Kaixin Ma, Alessandro Oltramari
Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge.
no code implementations • 31 Aug 2022 • Ehsan Qasemi, Alessandro Oltramari
Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners.
1 code implementation • COLING 2022 • Kaixin Ma, Filip Ilievski, Jonathan Francis, Eric Nyberg, Alessandro Oltramari
In this paper, we propose Coalescing Global and Local Information (CGLI), a new model that builds entity- and timestep-aware input representations (local input) considering the whole context (global input), and we jointly model the entity states with a structured prediction objective (global output).
no code implementations • 21 May 2022 • Jiarui Zhang, Filip Ilievski, Kaixin Ma, Jonathan Francis, Alessandro Oltramari
In this paper, we study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
no code implementations • 17 Jan 2022 • Alessandro Oltramari, Jonathan Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks.
1 code implementation • EMNLP 2021 • Kaixin Ma, Filip Ilievski, Jonathan Francis, Satoru Ozaki, Eric Nyberg, Alessandro Oltramari
In this paper, we investigate what models learn from commonsense reasoning datasets.
no code implementations • 12 Jan 2021 • Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely
Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.
no code implementations • 19 Dec 2020 • Yikang Li, Pulkit Goel, Varsha Kuppur Rajendra, Har Simrat Singh, Jonathan Francis, Kaixin Ma, Eric Nyberg, Alessandro Oltramari
Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models.
1 code implementation • 7 Nov 2020 • Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, Alessandro Oltramari
Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models.
no code implementations • 9 Mar 2020 • Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma, Ruwan Wickramarachchi
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI).
no code implementations • WS 2019 • Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg, Alessandro Oltramari
Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries.
Ranked #16 on
Common Sense Reasoning
on CommonsenseQA