no code implementations • 19 May 2023 • Seraphina Goldfarb-Tarrant, Adam Lopez, Roi Blanco, Diego Marcheggiani
To remedy this, we build a counterfactual evaluation corpus for gender and racial/migrant bias in four languages.
no code implementations • WS 2019 • Stefanos Angelidis, Lea Frermann, Diego Marcheggiani, Roi Blanco, Llu{\'\i}s M{\`a}rquez
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
no code implementations • 2 Oct 2019 • Stefanos Angelidis, Lea Frermann, Diego Marcheggiani, Roi Blanco, Lluís Màrquez
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
1 code implementation • EMNLP 2020 • Diego Marcheggiani, Ivan Titov
Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles.
no code implementations • IJCNLP 2019 • Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, Raquel Fernández
Information about individuals can help to better understand what they say, particularly in social media where texts are short.
2 code implementations • WS 2018 • Diego Marcheggiani, Laura Perez-Beltrachini
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
Ranked #2 on Data-to-Text Generation on SR11Deep
no code implementations • NAACL 2018 • Diego Marcheggiani, Jasmijn Bastings, Ivan Titov
Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods.
Ranked #8 on Machine Translation on WMT2016 English-German
no code implementations • EMNLP 2017 • Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an
We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation.
2 code implementations • EMNLP 2017 • Diego Marcheggiani, Ivan Titov
GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence.
Ranked #2 on Chinese Semantic Role Labeling on CoNLL-2009
2 code implementations • CONLL 2017 • Diego Marcheggiani, Anton Frolov, Ivan Titov
However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset.
1 code implementation • TACL 2016 • Diego Marcheggiani, Ivan Titov
We present a method for unsupervised open-domain relation discovery.
no code implementations • 19 Feb 2015 • Diego Marcheggiani, Fabrizio Sebastiani
While a lot of work has been devoted to devising learning methods that generate more and more accurate information extractors, no work has been devoted to investigating the effect of the quality of training data on the learning process.