The emergence of generative large language models (LLMs) raises the question: what will
be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring …

Annotated data is an essential ingredient in natural language processing for training and
evaluating machine learning models. It is therefore very desirable for the annotations to be …

Building conversational task-oriented bots requires large and diverse sets of annotated user
utterances to learn mappings between natural language utterances and user intents. Given …

We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in
which the dialog system mimics a troubleshooting agent who helps a user by diagnosing …

In large-scale commercial dialog systems, users express the same request in a wide variety
of alternative ways with a long tail of less frequent alternatives. Handling the full range of this …

Slot-filling models in task-driven dialog systems rely on carefully annotated training data.
However, annotations by crowd workers are often inconsistent or contain errors. Simple …

Background Data accuracy is essential for scientific research and policy development. The
National Violent Death Reporting System (NVDRS) data is widely used for discovering the …

Manually annotated datasets are crucial for training and evaluating Natural Language
Processing models. However, recent work has discovered that even widely-used benchmark …

Building task-oriented bots requires mapping a user utterance to an intent with its associated
entities to serve the request. Doing so is not easy since it requires large quantities of high …

Data accuracy is essential for scientific research and policy development. The National
Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns …