Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

Last update: Apr 25, 2022

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HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation

Place holder for data sharing for translation evaluation framework HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

File instruction: "Test22-scoring-10-9-2021.xlsm": the 111 segments of English-to-Russian MT using GoogleMT and another System, including data of human offered reference translations, scoring sheeting using HOPE evaluation framework. Open-source for non-profit research. For comercial usage, please contact: @copyright Logrus Global <serge.gladkoff [at] logrusglobal.com> https://logrusglobal.com

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Lifeng Han

natural language processing, machine translation, evaluation models, Mathematical modeling, athlete, writer.

GitHub Repository

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⚡ Fast • 🪶 Lightweight • 0️⃣ Dependency • 🔌 Pluggable • 😈 TLS interception • 🔒 DNS-over-HTTPS • 🔥 Poor Man's VPN • ⏪ Reverse & ⏩ Forward • 👮🏿 "Proxy Server" framework • 🌐 "Web Server" framework • ➵ ➶ ➷ ➠ "PubSub" framework • 👷 "Work" acceptor & executor framework

Table of Contents Features Install Using PIP Stable version Development version Using Docker Stable version Development version Using HomeBrew Stable

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Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

Related tags

Overview

HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation

Owner

Lifeng Han

An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022

Wind Speed Prediction using LSTMs in PyTorch

Fast, general, and tested differentiable structured prediction in PyTorch

(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

Trajectory Extraction of road users via Traffic Camera

Manifold-Mixup implementation for fastai V2

Official implementation of Few-Shot and Continual Learning with Attentive Independent Mechanisms

Data for "Driving the Herd: Search Engines as Content Influencers" paper

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

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