1 code implementation • INLG (ACL) 2020 • André Luiz Rosa Teixeira, João Campos, Rossana Cunha, Thiago castro Ferreira, Adriana Pagano, Fabio Cozman
This demo paper introduces DaMata, a robot-journalist covering deforestation in the Brazilian Amazon.
no code implementations • OSACT (LREC) 2022 • Salaheddin Alzubi, Thiago castro Ferreira, Lucas Pavanelli, Mohamed Al-Badrashiny
Abusive speech on online platforms has a detrimental effect on users’ mental health.
no code implementations • LREC 2022 • Kelvin Han, Thiago castro Ferreira, Claire Gardent
Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts.
1 code implementation • MSR (COLING) 2020 • Simon Mille, Anya Belz, Bernd Bohnet, Thiago castro Ferreira, Yvette Graham, Leo Wanner
As in SR’18 and SR’19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed.
no code implementations • ACL (WebNLG, INLG) 2020 • Thiago castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing).
no code implementations • INLG (ACL) 2021 • Simon Mille, Thiago castro Ferreira, Anya Belz, Brian Davis
Clarity had a higher degree of reproducibility than Fluency, as measured by the coefficient of variation.
no code implementations • LREC 2022 • Shreyas Sharma, Kareem Darwish, Lucas Pavanelli, Thiago castro Ferreira, Mohamed Al-Badrashiny, Kamer Ali Yuksel, Hassan Sawaf
The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties.
1 code implementation • INLG (ACL) 2021 • Thiago castro Ferreira, Helena Vaz, Brian Davis, Adriana Pagano
This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG.
no code implementations • RANLP 2021 • Thiago castro Ferreira, João Victor de Pinho Costa, Isabela Rigotto, Vitoria Portella, Gabriel Frota, Ana Luisa A. R. Guimarães, Adalberto Penna, Isabela Lee, Tayane A. Soares, Sophia Rolim, Rossana Cunha, Celso França, Ariel Santos, Rivaney F. Oliveira, Abisague Langbehn, Daniel Hasan Dalip, Marcos André Gonçalves, Rodrigo Bastos Fóscolo, Adriana Pagano
This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach.
no code implementations • RANLP 2021 • Felipe Araújo de Britto, Thiago castro Ferreira, Leonardo Pereira Nunes, Fernando Silva Parreiras
Written communication is of utmost importance to the progress of scientific research.
no code implementations • 13 Feb 2024 • Chinonso Cynthia Osuji, Thiago castro Ferreira, Brian Davis
Relevant literature in this field on datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods is reviewed.
1 code implementation • 20 Jan 2024 • Golara Javadi, Kamer Ali Yuksel, Yunsu Kim, Thiago castro Ferreira, Mohamed Al-Badrashiny
The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 14 Jul 2022 • Chris van der Lee, Thiago castro Ferreira, Chris Emmery, Travis Wiltshire, Emiel Krahmer
In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension.
no code implementations • COLING 2020 • Felipe Almeida Costa, Thiago castro Ferreira, Adriana Pagano, Wagner Meira
This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese.
no code implementations • COLING 2020 • Rossana Cunha, Thiago castro Ferreira, Adriana Pagano, Fabio Alves
Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data.
no code implementations • 16 Sep 2020 • Diego Moussallem, Dwaraknath Gnaneshwar, Thiago castro Ferreira, Axel-Cyrille Ngonga Ngomo
The RDF-to-text task has recently gained substantial attention due to continuous growth of Linked Data.
no code implementations • WS 2019 • Thiago Castro Ferreira, Emiel Krahmer
This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR{'}19) (Mille et al., 2019).
1 code implementation • RANLP 2019 • Florian Kunneman, Thiago castro Ferreira, Emiel Krahmer, Antal Van den Bosch
Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before.
1 code implementation • IJCNLP 2019 • Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, Emiel Krahmer
In contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in-between.
Ranked #8 on Data-to-Text Generation on WebNLG Full
1 code implementation • WS 2018 • Thiago Castro Ferreira, Diego Moussallem, Emiel Krahmer, S Wubben, er
This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a, b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.
1 code implementation • WS 2018 • Thiago Castro Ferreira, S Wubben, er, Emiel Krahmer
This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18).
1 code implementation • ACL 2018 • Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, Emiel Krahmer
Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function.
1 code implementation • LREC 2018 • Diego Moussallem, Thiago castro Ferreira, Marcos Zampieri, Maria Claudia Cavalcanti, Geraldo Xexéo, Mariana Neves, Axel-Cyrille Ngonga Ngomo
The generation of natural language from Resource Description Framework (RDF) data has recently gained significant attention due to the continuous growth of Linked Data.
no code implementations • WS 2017 • Thiago Castro Ferreira, Iv Paraboni, r{\'e}
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly.
no code implementations • WS 2017 • Thiago Castro Ferreira, Iacer Calixto, S Wubben, er, Emiel Krahmer
In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT).
no code implementations • 12 Apr 2017 • Thiago castro Ferreira, Ivandre Paraboni
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly.
no code implementations • EACL 2017 • Thiago Castro Ferreira, Emiel Krahmer, S Wubben, er
The model relies on the REGnames corpus, a dataset with 53, 102 proper name references to 1, 000 people in different discourse contexts.
no code implementations • WS 2016 • Thiago Castro Ferreira, S Wubben, er, Emiel Krahmer
no code implementations • NAACL 2016 • Thiago Castro Ferreira, Emiel Krahmer, S Wubben, er