Rodrigo Nogueira

research

∙ 08/18/2023

Predictive Authoring for Brazilian Portuguese Augmentative and Alternative Communication

Individuals with complex communication needs (CCN) often rely on augment...

0 Jayr Pereira, et al. ∙

research

∙ 07/11/2023

BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams

One common trend in recent studies of language models (LMs) is the use o...

0 Thales Sales Almeida, et al. ∙

research

∙ 07/10/2023

InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval

Recent work has explored Large Language Models (LLMs) to overcome the la...

0 Hugo Abonizio, et al. ∙

research

∙ 04/26/2023

A Personalized Dense Retrieval Framework for Unified Information Access

Developing a universal model that can efficiently and effectively respon...

0 Hansi Zeng, et al. ∙

research

∙ 04/16/2023

Sabiá: Portuguese Large Language Models

As the capabilities of language models continue to advance, it is concei...

5 Ramon Pires, et al. ∙

research

∙ 04/03/2023

Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval

The advent of multilingual language models has generated a resurgence of...

0 Jimmy Lin, et al. ∙

research

∙ 03/29/2023

Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams

The present study aims to explore the capabilities of Language Models (L...

14 Desnes Nunes, et al. ∙

research

∙ 03/28/2023

NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval

This paper reports on a study of cross-lingual information retrieval (CL...

0 Vitor Jeronymo, et al. ∙

research

∙ 01/25/2023

ExaRanker: Explanation-Augmented Neural Ranker

Recent work has shown that inducing a large language model (LLM) to gene...

0 Fernando Ferraretto, et al. ∙

research

∙ 01/04/2023

InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval

Recently, InPars introduced a method to efficiently use large language m...

12 Vitor Jeronymo, et al. ∙

research

∙ 12/19/2022

Visconde: Multi-document QA with GPT-3 and Neural Reranking

This paper proposes a question-answering system that can answer question...

0 Jayr Pereira, et al. ∙

research

∙ 12/12/2022

In Defense of Cross-Encoders for Zero-Shot Retrieval

Bi-encoders and cross-encoders are widely used in many state-of-the-art ...

0 Guilherme Rosa, et al. ∙

research

∙ 10/26/2022

NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost

The widespread availability of search API's (both free and commercial) b...

0 Thales Sales Almeida, et al. ∙

research

∙ 09/27/2022

mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark

Robust 2004 is an information retrieval benchmark whose large number of ...

0 Vitor Jeronymo, et al. ∙

research

∙ 09/22/2022

MonoByte: A Pool of Monolingual Byte-level Language Models

The zero-shot cross-lingual ability of models pretrained on multilingual...

0 Hugo Abonizio, et al. ∙

research

∙ 08/24/2022

Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models

The ability to extrapolate, i.e., to make predictions on sequences that ...

0 Mirelle Bueno, et al. ∙

research

∙ 08/09/2022

A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022

In this work we describe our submission to the product ranking task of t...

0 Vitor Jeronymo, et al. ∙

research

∙ 06/06/2022

No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval

Recent work has shown that small distilled language models are strong co...

0 Guilherme Moraes Rosa, et al. ∙

research

∙ 05/30/2022

Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task

Recent work has shown that language models scaled to billions of paramet...

0 Guilherme Moraes Rosa, et al. ∙

research

∙ 02/10/2022

InPars: Data Augmentation for Information Retrieval using Large Language Models

The information retrieval community has recently witnessed a revolution ...

0 Luiz Bonifacio, et al. ∙

research

∙ 02/07/2022

To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment

There has been mounting evidence that pretrained language models fine-tu...

0 Guilherme Moraes Rosa, et al. ∙

research

∙ 01/14/2022

Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents

A typical information extraction pipeline consists of token- or span-lev...

8 Ramon Pires, et al. ∙

research

∙ 09/04/2021

On the ability of monolingual models to learn language-agnostic representations

Pretrained multilingual models have become a de facto default approach f...

0 Leandro Rodrigues de Souza, et al. ∙

research

∙ 08/31/2021

mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset

The MS MARCO ranking dataset has been widely used for training deep lear...

0 Luiz Henrique Bonifacio, et al. ∙

research

∙ 05/14/2021

A cost-benefit analysis of cross-lingual transfer methods

An effective method for cross-lingual transfer is to fine-tune a bilingu...

0 Guilherme Moraes Rosa, et al. ∙

research

∙ 04/26/2021

Yes, BM25 is a Strong Baseline for Legal Case Retrieval

We describe our single submission to task 1 of COLIEE 2021. Our vanilla ...

0 Guilherme Moraes Rosa, et al. ∙

research

∙ 02/25/2021

Investigating the Limitations of Transformers with Simple Arithmetic Tasks

The ability to perform arithmetic tasks is a remarkable trait of human i...

0 Rodrigo Nogueira, et al. ∙

research

∙ 02/19/2021

Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Pyserini is an easy-to-use Python toolkit that supports replicable IR re...

0 Jimmy Lin, et al. ∙

research

∙ 01/14/2021

The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models

We propose a design pattern for tackling text ranking problems, dubbed "...

0 Ronak Pradeep, et al. ∙

research

∙ 10/22/2020

Scientific Claim Verification with VERT5ERINI

This work describes the adaptation of a pretrained sequence-to-sequence ...

0 Ronak Pradeep, et al. ∙

research

∙ 10/13/2020

Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retriev...

0 Jimmy Lin, et al. ∙

research

∙ 09/19/2020

Can questions summarize a corpus? Using question generation for characterizing COVID-19 research

What are the latent questions on some textual data? In this work, we inv...

0 Gabriela Surita, et al. ∙

research

∙ 08/20/2020

PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data

In natural language processing (NLP), there is a need for more resources...

0 Diedre Carmo, et al. ∙

research

∙ 08/20/2020

Lite Training Strategies for Portuguese-English and English-Portuguese Translation

Despite the widespread adoption of deep learning for machine translation...

1 Alexandre Lopes, et al. ∙

research

∙ 07/14/2020

Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset

We present Covidex, a search engine that exploits the latest neural rank...

0 Edwin Zhang, et al. ∙

research

∙ 05/05/2020

Query Reformulation using Query History for Passage Retrieval in Conversational Search

Passage retrieval in a conversational context is essential for many down...

0 Sheng-Chieh Lin, et al. ∙

research

∙ 04/23/2020

Rapidly Bootstrapping a Question Answering Dataset for COVID-19

We present CovidQA, the beginnings of a question answering dataset speci...

7 Raphael Tang, et al. ∙

research

∙ 04/10/2020

Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned

We present the Neural Covidex, a search engine that exploits the latest ...

0 Edwin Zhang, et al. ∙

research

∙ 04/04/2020

Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models

This paper presents an empirical study of conversational question reform...

0 Sheng-Chieh Lin, et al. ∙

research

∙ 03/18/2020

TTTTTackling WinoGrande Schemas

We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrand...

0 Sheng-Chieh Lin, et al. ∙

research

∙ 03/14/2020

Document Ranking with a Pretrained Sequence-to-Sequence Model

This work proposes a novel adaptation of a pretrained sequence-to-sequen...

0 Rodrigo Nogueira, et al. ∙

research

∙ 02/14/2020

Electricity Theft Detection with self-attention

In this work we propose a novel self-attention mechanism model to addres...

0 Paulo Finardi, et al. ∙

research

∙ 01/23/2020

Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

Citation recommendation systems for the scientific literature, to help a...

0 Rodrigo Nogueira, et al. ∙

research

∙ 11/11/2019

Meta Answering for Machine Reading

We investigate a framework for machine reading, inspired by real world i...

16 Benjamin Borschinger, et al. ∙

research

∙ 10/31/2019

Multi-Stage Document Ranking with BERT

The advent of deep neural networks pre-trained via language modeling tas...

0 Rodrigo Nogueira, et al. ∙

research

∙ 09/23/2019

Portuguese Named Entity Recognition using BERT-CRF

Recent advances in language representation using neural networks have ma...

0 Fábio Souza, et al. ∙

research

∙ 08/16/2019

Learning Representations and Agents for Information Retrieval

A goal shared by artificial intelligence and information retrieval is to...

0 Rodrigo Nogueira, et al. ∙

research

∙ 04/17/2019

Document Expansion by Query Prediction

One technique to improve the retrieval effectiveness of a search engine ...

0 Rodrigo Nogueira, et al. ∙

research

∙ 01/13/2019

Passage Re-ranking with BERT

Recently, neural models pretrained on a language modeling task, such as ...

0 Rodrigo Nogueira, et al. ∙

research

∙ 09/27/2018

Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation

We propose a method to efficiently learn diverse strategies in reinforce...

0 Rodrigo Nogueira, et al. ∙

Rodrigo Nogueira

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