Search Results for author: Lucas Chaves Lima

Found 6 papers, 1 papers with code

Automatic Fake News Detection: Are Models Learning to Reason?

1 code implementation ACL 2021 Casper Hansen, Christian Hansen, Lucas Chaves Lima

Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence.

Fact Checking Fake News Detection

University of Copenhagen Participation in TREC Health Misinformation Track 2020

no code implementations3 Mar 2021 Lucas Chaves Lima, Dustin Brandon Wright, Isabelle Augenstein, Maria Maistro

Our approach consists of 3 steps: (1) we create an initial run with BM25 and RM3; (2) we estimate credibility and misinformation scores for the documents in the initial run; (3) we merge the relevance, credibility and misinformation scores to re-rank documents in the initial run.

Language Modelling Misinformation +1

Multi-Head Self-Attention with Role-Guided Masks

no code implementations22 Dec 2020 Dongsheng Wang, Casper Hansen, Lucas Chaves Lima, Christian Hansen, Maria Maistro, Jakob Grue Simonsen, Christina Lioma

The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.

Machine Translation Text Classification

Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19

no code implementations25 Nov 2020 Lucas Chaves Lima, Casper Hansen, Christian Hansen, Dongsheng Wang, Maria Maistro, Birger Larsen, Jakob Grue Simonsen, Christina Lioma

This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.

Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings

no code implementations20 Mar 2019 Dongsheng Wang, Quichi Li, Lucas Chaves Lima, Jakob Grue Simonsen, Christina Lioma

In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i. e., that whether a phrase is compositional or non-compositional depends on its context.

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