Search Results for author: Francesco Piccinno

Found 8 papers, 1 papers with code

Table-To-Text generation and pre-training with TabT5

no code implementations17 Oct 2022 Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun

Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020).

Data-to-Text Generation Table-to-Text Generation

Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs

no code implementations EMNLP 2021 Pierre Marion, Paweł Krzysztof Nowak, Francesco Piccinno

On CSQA, our approach increases the coverage from $80\%$ to $96. 2\%$, and the LF execution accuracy from $70. 6\%$ to $75. 6\%$, with respect to previous state-of-the-art results.

Conversational Question Answering Knowledge Graphs +1

Answering Conversational Questions on Structured Data without Logical Forms

no code implementations IJCNLP 2019 Thomas Müller, Francesco Piccinno, Massimo Nicosia, Peter Shaw, Yasemin Altun

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 representation.

Question Answering

SWAT: A System for Detecting Salient Wikipedia Entities in Texts

no code implementations10 Apr 2018 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.

Revisiting Taxonomy Induction over Wikipedia

no code implementations COLING 2016 Amit Gupta, Francesco Piccinno, Mikhail Kozhevnikov, Marius Pa{\c{s}}ca, Daniele Pighin

Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network.

Information Retrieval

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