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Found 15 papers, 5 papers with code
Building Goal-oriented Document-grounded Dialogue Systems
In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation.
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems
High-level reasoning can be defined as the capability to generalize over knowledge acquired via experience, and to exhibit robust behavior in novel situations.
Traffic-Domain Video Question Answering with Automatic Captioning
Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Intelligent Transportation Systems.
A Study of Situational Reasoning for Traffic Understanding
Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure.
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge.
Intelligent Traffic Monitoring with Hybrid AI
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.
Coalescing Global and Local Information for Procedural Text Understanding
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).
An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs
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.
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks.
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models
In this paper, we investigate what models learn from commonsense reasoning datasets.
Dimensions of Commonsense Knowledge
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.
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection
Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models.
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
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.
Neuro-symbolic Architectures for Context Understanding
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).
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
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.
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Common Sense Reasoning
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