Today, most conversational agents are limited to simple tasks supported by standalone commands, such as getting directions or scheduling an appointment. To support more complex tasks, agents must be able to generalize from and combine the commands they already understand. This paper presents a new approach to designing conversational agents inspired by linguistic theory, where agents can execute complex requests interactively by combining commands through nested conversations. We demonstrate this approach in Iris, an agent that can perform open-ended data science tasks such as lexical analysis and predictive modeling. To power Iris, we have created a domain-specific language that transforms Python functions into combinable automata and regulates their combinations through a type system. Running a user study to examine the strengths and limitations of our approach, we find that data scientists completed a modeling task 2.6 times faster with Iris than with Jupyter Notebook.

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by learning a neural embedding across billions of words on the web. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated such as neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.