9 citations · 13 across the 3 of their papers we have counts for
4 papers
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment…
To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment
Guilherme Moraes Rosa, Ruan Chaves Rodrigues, Roberto de Alencar Lotufo +1
There has been mounting evidence that pretrained language models fine-tuned on large and diverse supervised datasets can transfer well to a variety of out-of-domain tasks. In this…
Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents
Ramon Pires, Fábio C. de Souza, Guilherme Rosa +2
A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline…
Yes, BM25 is a Strong Baseline for Legal Case Retrieval
Guilherme Moraes Rosa, Ruan Chaves Rodrigues, Roberto Lotufo +1
We describe our single submission to task 1 of COLIEE 2021. Our vanilla BM25 got second place, well above the median of submissions. Code is available at https://github.com/neuralm…