Milimili. Collecting Parallel Data via Crowdsourcing
We present a methodology for gathering a parallel corpus through crowdsourcing, which is more cost-effective than hiring professional translators, albeit at the expense of quality. Additionally, we have made available experimental parallel data collected for Chechen-Russian and Fula-English language pairs.
Code (1)
Similar Papers 제목 키워드 기반
Service registration chatbot: collecting and comparing dialogues from AMT workers and service’s users
Crowdsourcing is the go-to solution for data collection and annotation in the context of NLP tasks. Nevertheless, crowdsourced data is noisy by nature; the source is often unknown and additional validation work is perfor…
ChatbotText GenerationCRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues
Large corpora of task-based and open-domain conversational dialogues are hugely valuable in the field of data-driven dialogue systems. Crowdsourcing platforms, such as Amazon Mechanical Turk, have been an effective metho…
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?
Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focu…
Multiple-choiceNatural Language UnderstandingQuestion AnsweringCochlScene: Acquisition of acoustic scene data using crowdsourcing
This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing. The detailed process of crowdsourcing is explained, including planning, validation criteria, and actual user interfaces. As a res…
Acoustic Scene ClassificationScene ClassificationBandit-Based Task Assignment for Heterogeneous Crowdsourcing
We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is how to cope with the diversity of tasks an…
Diversity