Search Results for author:
Found 9 papers, 8 papers with code
Findings of the 2021 Conference on Machine Translation (WMT21)
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets.
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research.
Learning from Task Descriptions
Typically, machine learning systems solve new tasks by training on thousands of examples.
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.
Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes
As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering.
Ranked #25 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.
Extracting Scientific Figures with Distantly Supervised Neural Networks
Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction.
