4 citations · 7 across the 3 of their papers we have counts for
6 papers
Table-To-Text generation and pre-training with TabT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno +2
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020). A major limitation of these architectures…
What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?
Alice Shoshana Jakobovits, Francesco Piccinno, Yasemin Altun
High-quality datasets for task-oriented dialog are crucial for the development of virtual assistants. Yet three of the most relevant large scale dialog datasets suffer from one com…
Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs
Pierre Marion, Paweł Krzysztof Nowak, Francesco Piccinno
We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (…
Answering Conversational Questions on Structured Data without Logical Forms
Thomas Müller, Francesco Piccinno, Massimo Nicosia +2
We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate represen…
Generating Logical Forms from Graph Representations of Text and Entities
Peter Shaw, Philip Massey, Angelica Chen +2
Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate informa…
SWAT: A System for Detecting Salient Wikipedia Entities in Texts
Marco Ponza, Paolo Ferragina, Francesco Piccinno
We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document.…