  Natural language (NL) toolkits enable visualization developers, who may not
have a background in natural language processing (NLP), to create natural
language interfaces (NLIs) for end-users to flexibly specify and interact with
visualizations. However, these toolkits currently only support one-off
utterances, with minimal capability to facilitate a multi-turn dialog between
the user and the system. Developing NLIs with such conversational interaction
capabilities remains a challenging task, requiring implementations of low-level
NLP techniques to process a new query as an intent to follow-up on an older
query. We extend an existing Python-based toolkit, NL4DV, that processes an NL
query about a tabular dataset and returns an analytic specification containing
data attributes, analytic tasks, and relevant visualizations, modeled as a JSON
object. Specifically, NL4DV now enables developers to facilitate multiple
simultaneous conversations about a dataset and resolve associated ambiguities,
augmenting new conversational information into the output JSON object. We
demonstrate these capabilities through three examples: (1) an NLI to learn
aspects of the Vega-Lite grammar, (2) a mind mapping application to create
free-flowing conversations, and (3) a chatbot to answer questions and resolve
ambiguities.
