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Found 2 papers, 1 papers with code
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking.
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News
Specifically, we use bi-directional Recurrent Neural Networks, together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article.
Ranked #4 on
Fake News Detection
on FNC-1
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