  Current approaches to Natural Language Generation (NLG) for dialog mainly
focus on domain-specific, task-oriented applications (e.g. restaurant booking)
using limited ontologies (up to 20 slot types), usually without considering the
previous conversation context. Furthermore, these approaches require large
amounts of data for each domain, and do not benefit from examples that may be
available for other domains. This work explores the feasibility of applying
statistical NLG to scenarios requiring larger ontologies, such as multi-domain
dialog applications or open-domain question answering (QA) based on knowledge
graphs. We model NLG through an Encoder-Decoder framework using a large dataset
of interactions between real-world users and a conversational agent for
open-domain QA. First, we investigate the impact of increasing the number of
slot types on the generation quality and experiment with different partitions
of the QA data with progressively larger ontologies (up to 369 slot types).
Second, we perform multi-task learning experiments between open-domain QA and
task-oriented dialog, and benchmark our model on a popular NLG dataset.
Moreover, we experiment with using the conversational context as an additional
input to improve response generation quality. Our experiments show the
feasibility of learning statistical NLG models for open-domain QA with larger
ontologies.
