18 citations · 126 across the 28 of their papers we have counts for
10 papers · 1 filter
A Study of Situational Reasoning for Traffic Understanding
Jiarui Zhang, Filip Ilievski, Kaixin Ma +3
Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure. Understanding traffic situation…
Knowledge-enhanced Agents for Interactive Text Games
Prateek Chhikara, Jiarui Zhang, Filip Ilievski +2
Communication via natural language is a key aspect of machine intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of su…
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Jiarui Zhang, Filip Ilievski, Aravinda Kollaa +3
Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge. Prior work has provided sufficient perce…
Coalescing Global and Local Information for Procedural Text Understanding
Kaixin Ma, Filip Ilievski, Jonathan Francis +2
Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural unde…
An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs
Jiarui Zhang, Filip Ilievski, Kaixin Ma +2
Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models, in zero-shot evaluation on various…
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
Alessandro Oltramari, Jonathan Francis, Filip Ilievski +2
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for…
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Commonsense reasoning benchmarks have been largely solved by fine-tuning language models. The downside is that fine-tuning may cause models to overfit to task-specific data and the…
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection
Yikang Li, Pulkit Goel, Varsha Kuppur Rajendra +5
Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models. In this work, we specifically focus on the Commonge…
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that mo…
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Kaixin Ma, Jonathan Francis, Quanyang Lu +2
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 re…