545 citations · 576 across the 11 of their papers we have counts for
11 papers
mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations
Jonas Pfeiffer, Francesco Piccinno, Massimo Nicosia +3
Multilingual sequence-to-sequence models perform poorly with increased language coverage and fail to consistently generate text in the correct target language in few-shot settings.…
Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing
Massimo Nicosia, Francesco Piccinno
Token free approaches have been successfully applied to a series of word and span level tasks. In this work, we compare a byte-level (ByT5) and a wordpiece based (mT5) sequence to…
DePlot: One-shot visual language reasoning by plot-to-table translation
Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno +7
Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models req…
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
Fangyu Liu, Francesco Piccinno, Syrine Krichene +6
Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data.…
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 (…
TAPAS: Weakly Supervised Table Parsing via Pre-training
Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller +2
Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on…
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.…