We present NLU++, a novel dataset for natural language understanding (NLU) in task-
oriented dialogue (ToD) systems, with the aim to provide a much more challenging …

Most language understanding models in task-oriented dialog systems are trained on a small
amount of annotated training data, and evaluated in a small set from the same distribution …

Task oriented dialog agents provide a natural language interface for users to complete their
goal. Dialog State Tracking (DST), which is often a core component of these systems, tracks …

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 …

In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive,
or are unique: informative samples that improve model robustness. Identifying outliers can …

Many statistical models have high accuracy on test benchmarks, but are not explainable,
struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily …

Virtual Assistant technology is rapidly proliferating to improve productivity in a variety of
tasks. While several virtual assistants for everyday tasks are well-known (eg, Siri, Cortana …

Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity
as workers tend to write simple variations from prompts. We propose a general approach for …

The increasing volume of commercially available conversational agents (CAs) on the market
has resulted in users being burdened with learning and adopting multiple agents to …

Automated generation of conversational dialogue using modern neural architectures has
made notable advances. However, these models are known to have a drawback of often …