Search Results for author: Gaetano Rossiello

Found 11 papers, 6 papers with code

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

2 code implementations31 Aug 2021 Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.

Domain Adaptation Few-Shot Learning +3

Zero-shot Slot Filling with DPR and RAG

2 code implementations17 Apr 2021 Michael Glass, Gaetano Rossiello, Alfio Gliozzo

Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.

Knowledge Base Population Knowledge Graphs +1

Open Knowledge Graphs Canonicalization using Variational Autoencoders

1 code implementation8 Dec 2020 Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya, Sugato Bagchi, Alfio Gliozzo

In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.

Knowledge Graphs

Knowledge Graph Embeddings and Explainable AI

no code implementations30 Apr 2020 Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari, Pasquale Minervini

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.

Knowledge Graph Embeddings

Learning Relational Representations by Analogy using Hierarchical Siamese Networks

no code implementations NAACL 2019 Gaetano Rossiello, Alfio Gliozzo, Robert Farrell, Nicolas Fauceglia, Michael Glass

We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions.

Entity Embeddings Knowledge Base Population +2

Iterative Multi-document Neural Attention for Multiple Answer Prediction

no code implementations8 Feb 2017 Claudio Greco, Alessandro Suglia, Pierpaolo Basile, Gaetano Rossiello, Giovanni Semeraro

People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.

Information Seeking Question Answering +1

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