Search Results for author: Diego Marcheggiani

Found 13 papers, 5 papers with code

Book QA: Stories of Challenges and Opportunities

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.

BookQA: Stories of Challenges and Opportunities

no code implementations2 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.

Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling

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.

Semantic Role Labeling

You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP

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.

Graph Attention

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

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.

Machine Translation Translation

Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

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.

Machine Translation Translation

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

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.

Semantic Role Labeling

A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

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.

Semantic Role Labeling

On the Effects of Low-Quality Training Data on Information Extraction from Clinical Reports

no code implementations19 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.

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