activity
20212024
most citedEnvisioning a Human-AI collaborative system to transform policies into decision models

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

Description Boosting for Zero-Shot Entity and Relation Classification

Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo +3

Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks t…

cs.CL2023

Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction

Gabriele Picco, Marcos Martínez Galindo, Alberto Purpura +3

The Zero-Shot Learning (ZSL) task pertains to the identification of entities or relations in texts that were not seen during training. ZSL has emerged as a critical research area d…

cs.CL2023

Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models

Myles Foley, Ambrish Rawat, Taesung Lee +3

The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption. While the pre-trained models can perform many tasks, such model…

cs.LG2023★ 2 cited

Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery

Hoang Thanh Lam, Marco Luca Sbodio, Marcos Martínez Galindo +6

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases o…

cs.AI2022★ 2 cited

Envisioning a Human-AI collaborative system to transform policies into decision models

Vanessa Lopez, Gabriele Picco, Inge Vejsbjerg +8

Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for…

cs.CL2021

Ensembling Graph Predictions for AMR Parsing

Hoang Thanh Lam, Gabriele Picco, Yufang Hou +5

In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into depe…

cs.CL2021

Neural Unification for Logic Reasoning over Natural Language

Gabriele Picco, Hoang Thanh Lam, Marco Luca Sbodio +1

Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (fac…