How can I benefit from the grant?
- Any publication that relies on the data collected using the awarded funds should acknowledge that the study was supported by the Toloka research grant.
- The dataset collected in the experiment should be released publicly in the Toloka repository of datasets within 6 months after data collection ends.
- Toloka supports the Crowd Science Seminar, which covers a broad area of Human-AI collaboration. We encourage award recipients to give a talk at this seminar!
Success Stories
Philipp Chapkovski, Post-doctoral research fellow at Higher School of Economics:
“In our behavioral economics lab at the Higher School of Economics we use Toloka to run interactive human subject experiments. We create teams of several participants to observe how they communicate and make economic decisions together. Toloka helps us to run experiments that involve simultaneous participation of people from multiple regions. Toloka also provides instantaneous recruitment of specific groups of people and smooth processing of their payments.”
Mikhail Burtsev, Head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Pavel Braslavsky, Senior researcher at Combinatorial Algebra Lab of Ural Federal University:
“High-quality annotated data is crucial for measurable progress in NLP tasks. In the field of knowledge base question answering there are no reliable automatic annotation tools to date. At the same time, in-house data annotation makes the dataset creation process slow and expensive. Thus, we used Toloka as the core of our data annotation pipeline and were able to create high-quality datasets for Russian knowledge base and hybrid QA evaluation.”
Philipp Chapkovski, Post-doctoral research fellow at Higher School of Economics:
“In our behavioral economics lab at the Higher School of Economics we use Toloka to run interactive human subject experiments. We create teams of several participants to observe how they communicate and make economic decisions together. Toloka helps us to run experiments that involve simultaneous participation of people from multiple regions. Toloka also provides instantaneous recruitment of specific groups of people and smooth processing of their payments.”
Mikhail Burtsev, Head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Pavel Braslavsky, Senior researcher at Combinatorial Algebra Lab of Ural Federal University:
“High-quality annotated data is crucial for measurable progress in NLP tasks. In the field of knowledge base question answering there are no reliable automatic annotation tools to date. At the same time, in-house data annotation makes the dataset creation process slow and expensive. Thus, we used Toloka as the core of our data annotation pipeline and were able to create high-quality datasets for Russian knowledge base and hybrid QA evaluation.”