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Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other than English, where human evaluations are scarce. In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation. SEAHORSE consists of 96K summaries with human ratings along 6 dimensions of text quality: comprehensibility, repetition, grammar, attribution, main ideas, and conciseness, covering 6 languages, 9 systems and 4 datasets. As a result of its size and scope, SEAHORSE can serve both as a benchmark to evaluate learnt metrics, as well as a large-scale resource for training such metrics. We show that metrics trained with SEAHORSE achieve strong performance on the out-of-domain meta-evaluation benchmarks TRUE (Honovich et al., 2022) and mFACE (Aharoni et al., 2022). We make the SEAHORSE dataset and metrics publicly available for future research on multilingual and multifaceted summarization evaluation.

This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn't require a PhD, and you don't need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic. In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use: To do that we're going to train and use a Convolutional Neural Network (CNN). We're going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what's being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park. I've decided to write this on Github vs. as a blog post because I'm sure that some of what I've written below is misleading, naive, or just plain wrong.

The United Nations is a vast organization with a diverse set of technology needs. The organization is an umbrella under which numerous sub-organizations--UNHCR and UNICEF, for example--operate somewhat autonomously. The UN is charged with providing global humanitarian relief, resolving multinational disputes, and tracking global crime. Tech innovation--particularly big data--aids the UN's mission by making operations more efficient and providing critical operational insight. The UN generates and tracks information on a global scale but has trouble managing large piles of data and is staffed with policy makers who are not inherently tech-savvy.

This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn't require a PhD, and you don't need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic. In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use: To do that we're going to train and use a Convolutional Neural Network (CNN). We're going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what's being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park.