1 code implementation • 27 Jun 2024 • Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti
We then present a novel hallucination detection strategy that exploits the computational graph of the NSP model to detect the NSP hallucinations in the presence of ontology gaps, out-of-domain utterances, and to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and ~1%.
no code implementations • 13 Oct 2022 • Andy Rosenbaum, Saleh Soltan, Wael Hamza, Amir Saffari, Marco Damonte, Isabel Groves
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data.
no code implementations • Joint Conference on Lexical and Computational Semantics 2021 • Marco Damonte, Emilio Monti
The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ida Szubert, Marco Damonte, Shay B. Cohen, Mark Steedman
Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations.
2 code implementations • NAACL 2019 • Marco Damonte, Shay B. Cohen
AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs.
Ranked #2 on Graph-to-Sequence on LDC2015E86:
no code implementations • NAACL 2019 • Marco Damonte, Rahul Goel, Tagyoung Chung
Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response.
1 code implementation • 18 Sep 2018 • Joachim Fainberg, Ben Krause, Mihai Dobre, Marco Damonte, Emmanuel Kahembwe, Daniel Duma, Bonnie Webber, Federico Fancellu
Conversational agents are gaining popularity with the increasing ubiquity of smart devices.
no code implementations • NAACL 2018 • Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang
Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form.
no code implementations • 28 Sep 2017 • Ben Krause, Marco Damonte, Mihai Dobre, Daniel Duma, Joachim Fainberg, Federico Fancellu, Emmanuel Kahembwe, Jianpeng Cheng, Bonnie Webber
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition.
1 code implementation • NAACL 2018 • Marco Damonte, Shay B. Cohen
Abstract Meaning Representation (AMR) annotation efforts have mostly focused on English.
no code implementations • EACL 2017 • Renars Liepins, Ulrich Germann, Guntis Barzdins, Alex Birch, ra, Steve Renals, Susanne Weber, Peggy van der Kreeft, Herv{\'e} Bourlard, Jo{\~a}o Prieto, Ond{\v{r}}ej Klejch, Peter Bell, Alex Lazaridis, ros, Alfonso Mendes, Sebastian Riedel, Mariana S. C. Almeida, Pedro Balage, Shay B. Cohen, Tomasz Dwojak, Philip N. Garner, Andreas Giefer, Marcin Junczys-Dowmunt, Hina Imran, David Nogueira, Ahmed Ali, Mir, Sebasti{\~a}o a, Andrei Popescu-Belis, Lesly Miculicich Werlen, Nikos Papasarantopoulos, Abiola Obamuyide, Clive Jones, Fahim Dalvi, Andreas Vlachos, Yang Wang, Sibo Tong, Rico Sennrich, Nikolaos Pappas, Shashi Narayan, Marco Damonte, Nadir Durrani, Sameer Khurana, Ahmed Abdelali, Hassan Sajjad, Stephan Vogel, David Sheppey, Chris Hernon, Jeff Mitchell
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
4 code implementations • EACL 2017 • Marco Damonte, Shay B. Cohen, Giorgio Satta
We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.
Ranked #5 on AMR Parsing on LDC2015E86