Search Results for author:
Found 11 papers, 6 papers with code
Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models
In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets (generic models) predicted safety outcomes better than the company-specific models.
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metricsfor Automatic Text Generation
While traditional natural language generation metrics are fast, they are not very reliable.
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT.
Ranked #1 on
Text Summarization
on OrangeSum
(using extra training data)
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings
A valuable by-product of our method is the ability to sample, at no extra cost, sentences containing different senses of a given word.
Message Passing Attention Networks for Document Understanding
In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).
Ranked #1 on
Multi-Modal Document Classification
on Reuters-21578
document understanding
Multi-Modal Document Classification
+2
AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes
This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning.
Automatically Learning Construction Injury Precursors from Text
In light of the increasing availability of digitally recorded safety reports in the construction industry, it is important to develop methods to exploit these data to improve our understanding of safety incidents and ability to learn from them.
Perturb and Combine to Identify Influential Spreaders in Real-World Networks
Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure.
Word Embeddings for the Construction Domain
Our vectors were obtained by running word2vec on an 11M-word corpus that we created from scratch by leveraging freely-accessible online sources of construction-related text.
Construction Safety Risk Modeling and Simulation
By applying our methodology on an attribute and outcome dataset directly obtained from 814 injury reports, we show that the frequency-magnitude distribution of construction safety risk is very similar to that of natural phenomena such as precipitation or earthquakes.
