Search Results for author:
Found 11 papers, 8 papers with code
Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
This approach empowers domain experts, even those not well-versed in ML/AI, to formally declare their knowledge to be incorporated in customized neural models in the DomiKnowS framework.
Consistent Joint Decision-Making with Heterogeneous Learning Models
This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge.
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
The Role of Semantic Parsing in Understanding Procedural Text
In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text.
CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date.
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
We demonstrate a library for the integration of domain knowledge in deep learning architectures.
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events
This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding.
Ranked #1 on
Procedural Text Understanding
on ProPara
SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
Latent Alignment of Procedural Concepts in Multimodal Recipes
We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA.
Ranked #1 on
Question Answering
on RecipeQA
Hybrid-Learning approach toward situation recognition and handling
Obtaining knowledge from the environment is often through sensors, and the response to a particular circumstance is offered by actuators.
