Papers by Thiago Pardo

8 papers
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)

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Challenge: Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) .
Approach: They present a review of the literature on Natural Language Generation in Brazilian Portuguese.
Outcome: The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions.
PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model (2022.lrec-1)

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Challenge: lexical resource for Brazilian Portuguese with 1,221,218 entries, according to the Universal Dependencies model and guidelines.
Approach: They propose to build a large and freely available lexicon for Portuguese that delivers morphosyntactic information according to the Universal Dependencies model.
Outcome: The proposed lexical resource has high language coverage and good quality data.
Semantically Inspired AMR Alignment for the Portuguese Language (2020.emnlp-main)

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Challenge: Abstract Meaning Representation (AMR) parsers require alignment between nodes and words of the sentence.
Approach: They propose to use a more semantically matched word-concept pair to align graphs with words in Portuguese . they performed intrinsic and extrinsic evaluations and found it outperforms the English alignment strategies.
Outcome: The proposed method outperforms the existing methods for English and achieves competitive results with a parser designed for the Portuguese language.
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)

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Challenge: Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area.
Approach: They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach.
Outcome: The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues.
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)

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Challenge: In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content.
Approach: They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language.
Outcome: The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement.
Measuring the Impact of Readability Features in Fake News Detection (2020.lrec-1)

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Challenge: Recent efforts to detect fake news use language-based approaches to detect news articles . authors show that readability features can improve classification accuracy .
Approach: They propose to use readability features to detect fake news in the Brazilian Portuguese language . they show that such features can achieve up to 92% classification accuracy .
Outcome: The proposed features achieve up to 92% accuracy and may improve previous classification results.
Rhetorical Structure Approach for Online Deception Detection: A Survey (2022.lrec-1)

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Challenge: Existing studies on how people use language to inform and misinform are relevant.
Approach: They analyze how discourse structure is applied to fake news detection on the web and social media.
Outcome: The proposed framework is applied to fake news and fake reviews detection on the web and social media.
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)

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Challenge: Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively.
Approach: They propose to use Brazilian Portuguese and Abstract Meaning Representation as semantic representations for NLG.
Outcome: The proposed methods were evaluated on two datasets (one automatically generated and another human-generated) to compare the performance in a real context.

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