"Feet-on-street Crowdsourcing"

Speaker: Evgenii Konovalov, Yandex

"Feet-on-street crowdsourcing"
Speaker: Evgenii Konovalov, Yandex
About the talk
This talk will introduce feet-on-street crowdsourcing, also known as spatial crowdsourcing, which implies performing the given task in a real-world location. We will overview the successful applications of spatial crowdsourcing in Yandex company and outside. Also, we will discuss the specifics of the feet-on-street tasks and managing them in human-in-the-loop pipelines.

Come and learn how feet-on-street crowdsourcing helps to digitalize the offline world!
We are hosting a seminar series where crowd researchers can discuss their work, exchange ideas, and establish new collaborations.
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Talk title


Image Credit: Gregory Willson
NLP team of Skolkovo Institute of Science and Technology trained Neural Network which will help dialogue agents not to discuss "sensitive" topic in dangerous manner.

Problem: neural networks do not understand what they say. After training on big text datasets from social networks, they can suddenly begin to justify slavery and racism, as well as tell a person the right ways to harm themselves.

Solution: a neural model that detects "inappropriate" statements related to these "sensitive" topics for further content filtering.

So far, 18 topics were defined as "sensitive", including drugs, pornography, politics, religion, suicide and crime. The main criterion of relevance is whether the statement can harm the human interlocutor or spoil the reputation of the chatbot owner. The data for training the neural network was taken from Dvach and OtvetiMail.ru.

Official press release: https://www.skoltech.ru/en/2021/07/neural-model-seeks-inappropriateness-to-reduce-chatbot-awkwardness/

Published article about the research for deeper understanding: https://aclanthology.org/2021.bsnlp-1.4/
Pre-trained model for inappropriate utterances detection: https://huggingface.co/Skoltech/russian-inappropriate-messages