DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

Related tags

Deep Learningdeepage
Overview

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

This repository is related to the paper DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment, by Flávio Nakasato Cação1, Marcos Menon José1, André Seidel Oliveira1, Stefano Spindola1, Anna Helena Reali Costa1 and Fábio Gagliardi Cozman1. It was accepted at the Main Track of the 10th Brazilian Conference on Intelligent System (BRACIS'21).

Abstract: The challenge of climate change and biome conservation is one of the most pressing issues of our time - particularly in Brazil, where key environmental reserves are located. Given the availability of large textual databases on ecological themes, it is natural to resort to question answering (QA) systems to increase social awareness and understanding about these topics. In this work, we introduce multiple QA systems that combine in novel ways the BM25 algorithm, a sparse retrieval technique, with PTT5, a pre-trained state-of-the-art language model. Our QA systems focus on the Portuguese language, thus offering resources not found elsewhere in the literature. As training data, we collected questions from open-domain datasets, as well as content from the Portuguese Wikipedia and news from the press. We thus contribute with innovative architectures and novel applications, attaining an F1-score of 36.2 with our best model.

Acknowledgments

This work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Finance Code 001) and by the Itaú Unibanco S.A., through the Programa de Bolsas Itaú (PBI) of the Centro de Ciência de Dados (C 2 D) of Escola Politécnica of Universidade de São Paulo (USP). We also gratefully acknowledge support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grants 312180/2018-7 and 310085/2020-9) and the Center for Artificial Intelligence (C4AI-USP), with support by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant 2019/ 07665-4) and by the IBM Corporation.



1 Polytechnic School, University of Sao Paulo, Sao Paulo, Brazil

A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection Implementation of the Uniform DL Representation for AD algorithm describ

Paul Irofti 1 Nov 23, 2022
Galileo library for large scale graph training by JD

近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提

JD Galileo Team 128 Nov 29, 2022
This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google.

This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google.

75 Dec 02, 2022
Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop Hongwe

Hongwen Zhang 450 Dec 28, 2022
A configurable, tunable, and reproducible library for CTR prediction

FuxiCTR This repo is the community dev version of the official release at huawei-noah/benchmark/FuxiCTR. Click-through rate (CTR) prediction is an cri

XUEPAI 397 Dec 30, 2022
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Fuhang 5 Jan 18, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
render sprites into your desktop environment as shaped windows using GTK

spritegtk render static or animated sprites into your desktop environment as dynamic shaped windows using GTK requires pycairo and PYGobject: pip inst

hermit 20 Oct 27, 2022
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Bayesian Methods for Hackers Using Python and PyMC The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chap

Cameron Davidson-Pilon 25.1k Jan 02, 2023
TensorLight - A high-level framework for TensorFlow

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced feature

Benjamin Kan 10 Jul 31, 2022
ROS-UGV-Control-Interface - Control interface which can be used in any UGV

ROS-UGV-Control-Interface Cam Closed: Cam Opened:

Ahmet Fatih Akcan 1 Nov 04, 2022
This is the dataset for testing the robustness of various VO/VIO methods

KAIST VIO dataset This is the dataset for testing the robustness of various VO/VIO methods You can download the whole dataset on KAIST VIO dataset Ind

1 Sep 01, 2022
Avalanche RL: an End-to-End Library for Continual Reinforcement Learning

Avalanche RL: an End-to-End Library for Continual Reinforcement Learning Avalanche Website | Getting Started | Examples | Tutorial | API Doc | Paper |

ContinualAI 43 Dec 24, 2022
PyTorch implementation of Barlow Twins.

Barlow Twins: Self-Supervised Learning via Redundancy Reduction PyTorch implementation of Barlow Twins. @article{zbontar2021barlow, title={Barlow Tw

Facebook Research 839 Dec 29, 2022
This repository contains the map content ontology used in narrative cartography

Narrative-cartography-ontology This repository contains the map content ontology used in narrative cartography, which is associated with a submission

Weiming Huang 0 Oct 31, 2021
Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Pruner for nested cross-validation - Sphinx-Doc Nested cross-validation is necessary to avoid biased model performance in embedded feature selection i

1 Dec 15, 2021
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022